MLI Publications
To view the publications from a specific year, select the year
from the list below:
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Executive Administrative Assistant
Machine Learning and Inference Laboratory
Science & Tech II, Room 413
George Mason University
Fairfax, Virginia 22030-4444
Phone: (703) 993-1719
FAX: (703) 993-3729
Email: admin@mli.gmu.edu
MLI Publications are located in two files: MLI Pubs 69-87, containing publications for the period 1969-1987 (University of Illinois) and MLI Pubs, containing publications for the period 1988-present (George Mason University). Below is a list of MLI Publications for the period 1988 onward. To select publications for the period 1969 to 1987, click MLI Pubs 69-87.
To view a list of the publications available for download on-line, click on-line publications
P88-1
Michalski, R.S. and Watanabe, L., "Constructive Closed-Loop
Learning: Fundamental Ideas and Examples," Reports of the
Machine Learning and Inference Laboratory, MLI 88-1, School
of Information Technology and Engineering, George Mason
University, Fairfax, VA, 1988.
P88-2
Ko, H. and Michalski, R.S., "Types of Explanation and Their
Role in Constructive Closed-loop Learning," Reports of
the Machine Learning and Inference Laboratory, MLI 88-2,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, 1988.
P88-3
Mozetic, I. and Lavrac, N., "Incremental Learning from
Examples in a Logic-Based Formalism," Proceedings of
Machine Learning, Meta-Reasoning and Logic Workshop, Sesimbra,
Portugal, February 1988.
P88-4
Michalski, R.S. and Ko, H., "On the Nature of Explanation or
Why Did the Wine Bottle Shatter," Proceedings of the
Spring Symposium Series: Explanation-Based Learning, Stanford
University, pp. 12-21, March 1988.
P88-5
Char, J.M., Cherkassky, V., Wechsler, H. and Zimmerman, G.L.,
"Distributed and Fault-Tolerant Computation for Retrieval
Tasks Using Distributed Associative Memories," IEEE
Transactions on Computers, Vol. 37, No. 4, pp. 484-490, April
1988.
P88-6
Ko, H., "Empirical Assembly Planning: A Learning Approach,"
Ph.D. Dissertation, University of Illinois, Urbana-Champaign, May
1988.
P88-7
Stepp, R., Whitehall, B.L. and Holder, L.B., "Toward
Intelligent Machine Learning Algorithms," Reports of
Coordinated Science Laboratory, UILU-ENG-88-2221, College of
Engineering, University of Illinois, Urbana-Champaign, May 1988.
P88-8
Holder, L.B., "Discovering Substructure In Examples," Reports
of Coordinated Science Laboratory, UILU-ENG-88-2223, College
of Engineering, University of Illinois, Urbana-Champaign, May
1988.
P88-9
Stepp, R.E., "Machine Learning from Structured Objects,"
Reports of Coordinated Science Laboratory, pp. 353-363,
University of Illinois, Urbana-Champaign, May 1988.
P88-10
Holder, L.B., "Substructure Discovery in SUBDUE," Reports
of Coordinated Science Laboratory, UILU-ENG-88-2220, College
of Engineering, University of Illinois, Urbana-Champaign, May
1988.
P88-11
Whitehall, B.L., "Substructure Discovery of Macro-Operators,"
Reports of Coordinated Science Laboratory, UILU-ENG-88-2219,
College of Engineering, University of Illinois, Urbana-Champaign,
May 1988.
P88-12
Nowicki, A.R., "A Methodology for Representing Natural
Language Expressions in Variable-Valued Logic," Reports
of the Machine Learning and Inference Laboratory, MLI 88-3,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, June 1988.
P88-13
Greene, G.H., "The Abacus.2 System for Quantitative
Discovery: Using Dependencies to Discover Non-Linear Terms,"
Reports of the Machine Learning and Inference Laboratory,
MLI 88-4, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, June 1988.
P88-14
De Jong, K.A. and Schultz, A.C., "Using Experience-Based
Learning in Game Playing," Proceedings of the Fifth
International Conference on Machine Learning, Ann Arbor, MI,
Oxford: Clarendon Press, pp. 284-290, June 1988.
P88-15
Dontas, K., "APPLAUSE: An Implementation of the Collins-Michalski
Theory of Plausible Reasoning," M.S. Thesis, Computer
Science Department, University of Tennessee, Knoxville, TN,
August 1988.
P88-16
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "A
General Criterion for Measuring Quality of Concept Descriptions,"
Reports of the Machine Learning and Inference Laboratory,
MLI 88-5, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, October 1988.
P88-17
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "Measuring
Quality of Concept Descriptions," Proceedings of the
Third European Working Session on Learning, Glasgow, pp. 1-14,
October 1988.
P88-18
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "Representing
and Acquiring Imprecise and Context-Dependent Concepts in
Knowledge-based Systems," Proceedings of the 3rd
International Symposium on Methodologies for Intelligent Systems,
Turin, Italy, pp. 270-280, October 1988.
P88-19
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "Learning
Two-Tiered Descriptions of Flexible Concepts: A Method Employing
Examples of Varied Typicality and A Two-staged Construction of
the Base Concept Representation Part I: Principles and Methodology," Reports of the Machine Learning and
Inference Laboratory, School of Information Technology and
Engineering, George Mason University, MLI 88-6, Fairfax, VA,
November 1988.
P88-20
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "Learning
Two-Tiered Descriptions of Flexible Concepts: A Method Employing
Examples of Varied Typicality and A Two-staged Construction of
the Base Concept Representation Part II: Algorithms and Experiments," Reports of the Machine Learning and
Inference Laboratory, MLI 88-7, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
November 1988.
P88-21
Collins, A. and Michalski, R.S., "The Logic of Plausible
Reasoning: A Core Theory," Reports of the Machine
Learning and Inference Laboratory, MLI 88-8, School of
Information Technology and Engineering, George Mason University,
Fairfax, VA, November 1988.
P88-22
Wechsler, H. and Zimmerman, G.L., "2-D Invariant Object
Recognition Using Distributed Associative Memories," IEEE
Transactions on Pattern Analysis and Machine Intelligence,
Vol. 10, No. 6, pp. 811-821, November 1988.
P88-23
Stefanski, P.A., "An Introduction to the Computer Facilities
of the GMU Center for Artificial Intelligence," Reports
of the Machine Learning and Inference Laboratory, MLI 88-9,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, November 1988.
P88-24
Reinke, R.E. and Michalski, R.S., "Incremental Learning of
Concept Descriptions: A Method and Experimental Results,"
Hayes, J.E., Michie, D. and Richards, J. (Eds.), Machine
Intelligence II, Oxford: Clarendon Press, pp. 263-288, 1988.
P88-25
Sinclair, J.B. and Michalski, R.S., "Computer-Based
Consulting System For Diagnosing Soybean Diseases," Depts.
of Plant Pathology and Computer Science, University of Illinois,
Urbana-Champaign, 1988.
P88-26
Wechsler, H. and Zimmerman, L., "Distributed Associative
Memories and Data Fusion," Proceedings of the IEEE Second
International Conference on Neural Networks, Boston, MA,
November, 1988.
P88-27
Channic, T., "TEXPERT: An Application of Machine Learning to
Texture Recognition," M.S. Thesis, University of Illinois,
Urbana-Champaign, 1988.
P88-28
Carbonell, J.G., Michalski, R.S. and Mitchell, T.M., "Machine
Learning: A Historical and Methodological Analysis," Readings
from AI Magazine, Vols. 1-5, 1980-1985, R. Engelmore (Ed.),
Menlo Park, CA: American Association for Artificial Intelligence,
pp. 400-408, 1988./
P88-29
Bratko, I., Mozetic, I. and Lavrac, N., "Automatic Synthesis
and Compression of Cardiological Knowledge," J.E. Hayes, D.
Michie, J. Richards (Eds.) Machine Intelligence II, Oxford:
Clarendon Press, pp. 435-454, 1988.
P88-30
Pipitone, F., De Jong, K.A., Spears, W. and Marrone, M., "The
FIS Electronics Troubleshooting Project," Expert Systems
Applications to Telecommunications, Liebowitz (Ed.), Wiley
and Sons, pp. 73-101, 1988.
P88-31
De Jong, K.A., "Learning with Genetic Algorithms: An
Overview," Machine Learning, Vol. 3, pp. 121-138,
1988.
P88-32
Michalski, R.S., "On the Nature of Learning: Problems and
Research Directions," Informatyka Part 1, No. 2 and Informatyka
Part 3, No. 3, (Translators: E. Pierzchala and P. Zielczynski),
1988.
P88-33
Michalski, R.S., Ko, H. and Chen, K., "Qualitative
Prediction: SPARC/G Methodology for Inductively Describing and
Predicting Discrete Processes," in Van Lamsweerde, A. and
Dufour, O. (eds.), Current Issues in Expert Systems, 1988.
P88-34
Medin, D., Wattenmaker, W.D. and Michalski, R.S., "Constraints
and Preferences in Inductive Learning: An Experimental Study
Comparing Human and Machine Performance," Cognitive
Science, 1988.
P88-35
Michalski, R.S., "Learning Strategies and Automated
Knowledge Acquisition: An Overview," Chapter in Bolc, L. (ed.),
Computational Models of Learning, 1988.
P88-36
Michalski, R.S. and Ko, H., "On the Nature of Explanation,"
Proceedings of the Symposium on the Explanation-based Learning,
Stanford University, March 21-23, 1988.
P88-37
Antsaklis, P.J., De Jong, K.A., Meyrowitz, A.L. Meystel, A.,
Michalski, R.S., and Sutton, R.S., "Machine Learning in a
Dynamic World: Panal Discussion" (edited by M. Kokar), Proceedings
of the IEEE International Symposium on Intelligent Control,
Arlington, VA, August 24-26, 1988.
P89-1
Kodratoff, Y. "Characterizing Machine Learning Programs: A
European Compilation," Reports of the Machine Learning
and Inference Laboratory, MLI 89-1, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
February 1989.
P89-2
Stefanski, P.A. and Wnek, J., "Bibliography Maintenance
System," Reports of the Machine Learning and Inference
Laboratory, MLI 89-2, School of Information Technology and
Engineering, George Mason University, Fairfax, VA, March 1989.
P89-3
Carpineto, C., "Inductive Refinement of Causal Theories,"
Reports of the Machine Learning and Inference Laboratory,
MLI 89-3, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, March 1989.
P89-4
Mozetic, I., "Hierarchical Model-Based Diagnosis," Reports
of the Machine Learning and Inference Laboratory, MLI 89-4,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, April 1989.
P89-5
Pachowicz, P.W., "Comparison of Small Autonomous Robots by
the Analysis of Their Functional Components," Reports of
the Machine Learning and Inference Laboratory, MLI 89-5,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, 1989.
P89-6
Swangwanna, S. and Zytkow, J.M., "Real-Time Decision Making
for Autonomous Flight Control," SAE Technical Paper
Series, 891053, General Aviation Aircraft Meeting &
Exposition, Wichita, Kansas, pp. 1-7, April 1989.
P89-7
De Jong, K.A. and Spears, W.A., "Using Genetic Algorithms to
Solve NP-Complete Problems," Proceedings of the Third
International Conference on Genetic Algorithms and their
Applications, pp. 124-132, George Mason University, Fairfax,
VA, June 1989.
P89-8
Kelly, Jr., J.D., "PRS: A System for Plausible Reasoning,"
M.S. Thesis, University of Illinois, Urbana-Champaign, 1989.
P89-9
Zhang, J. and Michalski, R.S., "A Description of Preference
Criterion in Constructive Learning: A Discussion of Basic Issues,"
Proceedings of the 6th International Workshop on Machine
Learning, A. Segre (Ed.), Cornell University, Ithaca, NY,
Morgan Kaufmann, pp. 17-19, June 1989.
P89-10
Tecuci, G. and Kodratoff, Y., "Multi-strategy Learning in
Non-homogeneous Domain Theories," Proceedings of the 6th
International Workshop on Machine Learning, A. Segre (Ed.),
Cornell University, Ithaca, NY, Morgan Kaufmann, pp. 14-16, June
1989.
P89-11
Stephanou, H.E. and Erkmen, A.M., "Shape and Curvature Data
Fusion by Conductivity Analysis," NATO ARW Multisensor
Fusion for Computer Vision, Grenoble, France, June 1989.
P89-12
Wechsler, H. and Zimmerman, G.L., "Distributed Associative
Memory (DAM) for Bin-Picking," IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 11, No. 8, pp.
814-822, August 1989.
P89-13
Kaufman, K.A., Michalski, R.S. and Kerschberg, L., "Mining
for Knowledge in Databases: Goals and General Description of the
INLEN System," Proceedings of IJCAI-89 Workshop on
Knowledge Discovery in Databases, Detroit, MI, August 1989.
P89-14
Michalski, R.S. and Littman, D.C., "Future Directions of AI
in a Resource-Limited Environment," Proceedings of IJCAI-89
Workshop on Knowledge Discovery in Databases, Detroit, MI,
August 1989.
P89-15
Yegenoglu, F. and Stephanou, H.E., "Collision-Free Path
Planning for Multi-robot Systems," Proceedings of the
IEEE International Symposium on Intelligent Control, Albany,
NY, September 1989.
P89-16
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "Learning
Flexible Concepts Through a Search for Simpler but Still Accurate
Descriptions," Proceedings of the Fourth AAAI-Sponsored
Knowledge Acquisition for Knowledge-Based Systems Workshop,
Banff, Canada, pp. 1-10, October 1989.
P89-17
Michalski, R.S., Dontas, K. and Boehm-Davis, D., "Plausible
Reasoning: An Outline of Theory and Experiments," Proceedings
of the Fourth International Symposium on Methodologies for
Intelligent Systems, Charlotte, NC, pp. 17-19, October 1989.
P89-18
Pachowicz, P.W., "Low-Level Numerical Characteristics and
Inductive Learning Methodology in Texture Recognition," Proceedings
of the IEEE International Workshop on Tools for Artificial
Intelligence, Fairfax, VA, pp. 91-98, October, 1989.
P89-19
Stefanski, P.A. and Zytkow, J.A., "A Multisearch Approach to
Sequence Prediction," Proceedings of the Fourth
International Symposium on Methodologies for Intelligent Systems,
Charlotte, NC, pp. 359-366, October 1989.
P89-20
Michalski, R.S., "Multistrategy Constructive Learning:
Toward a Unified Theory of Learning," Proceedings of ONR
Workshop on Knowledge Acquisition, Arlington, VA, November
1989.
P89-21
Zytkow, J.M. and Pachowicz, P.W., "Fusion of Vision and
Touch for Spatio-temporal Reasoning in Learning Manipulation
Tasks," SPIE Symposium on Intelligent Robotics Systems,
Philadelphia, PA, November 1989.
P89-22
Zhang, J. and Michalski, R.S., "Rule Optimization Via SG-TRUNC
Method," Proceedings of the Fourth European Working
Session on Learning, December 1989.
P89-23
Collins, A. and Michalski, R.S., "The Logic of Plausible
Reasoning: A Core Theory," Cognitive Science, Vol. 13,
pp. 1-49, 1989.
P89-24
De Jong, K.A., "An Artificial Intelligence Approach to
Analog Systems Diagnosis," in Testing and Diagnosis of
Analog Systems, Van Nostrand-Reinhold, 1989.
P89-25
Baskin, A.B. and Michalski, R.S., "An Integrated Approach to
the Construction of Knowledge-Based Systems: Experience with
ADVISE and Related Programs," Topics in Expert System
Design, G. Guida and C. Tasso (Eds.), New York: North-Holland,
pp. 111-143, 1989.
P89-26
Kaufman, K., Michalski, R.S., Zytkow, J. and Kerschberg, L., "The INLEN System for Extracting Knowledge from Databases:
Goals and General Description," Reports of the Machine
Learning and Inference Laboratory, MLI 89-6, School of
Information Technology and Engineering, George Mason University,
Fairfax, VA, 1989.
P89-27
Kaufman, K., Michalski, R.S. and Schultz, A., "EMERALD 1: An
Integrated System of Machine Learning and Discovery Programs for
Education and Research, User's Guide," Reports of the
Machine Learning and Inference Laboratory, MLI 89-7, School
of Information Technology and Engineering, George Mason
University, Fairfax, VA, 1989.
P89-28
Fermanian, T.W., Michalski, R.S., Katz, B. and Kelly, J., "AGASSISTANT:
An Artificial Intelligence System for Discovering Patterns in
Agricultural Knowledge and Creating Diagnostic Advisory Systems,"
Agronomy Journal, Vol. 81, No. 2, pp. 306-312, 1989.
P89-29
Fermanian, T.W. and Michalski, R.S., "WEEDER: An Advisory
System for the Identification of Grasses in Turf," Agronomy
Journal, Vol. 81, No. 2, pp. 313-316, 1989.
P89-30
Michalski, R.S., "Two-Tiered Concept Meaning, Inferential
Matching and Conceptual Cohesiveness," Vosniadou, S. and
Ortony, A. (eds.), Similarity and Analogical Reasoning,
New York: Cambridge University Press, 1989.
P89-31
Ko, H., "Empirical Assembly Planning: A Learning Approach,"
Reports of the Machine Learning and Inference Laboratory,
MLI 89-8, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, 1989.
P89-32
Kodratoff, Y. and Tecuci, G., "The Central Role of
Explanations in DISCIPLE," in Morik, K. (ed.), Knowledge
Representation Organization in Machine Learning, Springer
Verlag, Berlin, pp. 135-147, 1989.
P89-33
Nguyen, T.N. and Stephanou, H.E., "A Continuous Model of
Robot Hand Preshaping," Proceedings of IEEE International
Conference on Systems, Man and Cybernetics, Boston, MA,
November 1989.
P89-34
Erkmen, A.M. and Stephanou, H.E., "Preshape Jacobians for
Minimum Momentum Grasping," Proceedings of IEEE
International Conference on Systems, Man and Cybernetics,
Boston, MA, November 1989.
P89-35
Erkmen, A.M. and Stephanou, H.E., "Multiresolutional Sensor
Fusion by Conductivity Analysis," Proceedings of SPiE
Symposium on Advances in Intelligent Robotics Systems,
Philadelphia, PA, November 1989.
P90-1
Michalski, R.S., "Multistrategy Constructive Learning:
Toward a Unified Theory of Learning," Reports of the
Machine Learning and Inference Laboratory, MLI 90-1, School
of Information Technology and Engineering, George Mason
University, Fairfax, VA, January 1990.
P90-2
Wnek, J., Sarma, J., Wahab, A. and Michalski, R.S., "Comparing
Learning Paradigms via Diagrammatic Visualization: A Case Study
in Single Concept Learning using Symbolic, Neural Net and Genetic
Algorithm Methods," Reports of the Machine Learning and
Inference Laboratory, MLI 90-2, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
January 1990.
P90-3
Wollowski, M., "Learning ICI-Rules through Reporting
Differences," Reports of the Machine Learning and
Inference Laboratory, MLI 90-3, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
January 1990.
P90-4
Stefanski, P.A., Wnek, J. and Zhang, J., "Bibliography of
Recent Machine Learning Research 1985-1989," Reports of
the Machine Learning and Inference Laboratory, MLI 90-4,
School of Information Technology and Engineering, George Mason
University, January 1990.
P90-5
Boehm-Davis, D., Dontas, K. and Michalski, R.S., "A
Validation and Exploration of Structural Aspects of the Collins-Michalski
Theory of Plausible Reasoning," Reports of the Machine
Learning and Inference Laboratory, MLI 90-5, School of
Information Technology and Engineering, George Mason University,
January 1990.
P90-6
De Jong, K.A., "Using Neural Networks and Genetic Algorithms
as Heuristics for NP-Complete Problems," Proceedings of
IJCNN-90, Washington D.C., January, 1990.
P90-7
De Jong, K.A., "FIS: An AI-based Fault Isolation System,"
Proceedings of IEEE Southeastern '90, New Orleans, LA,
March 1990.
P90-8
Piotrowski, T., "On Applying Artificial Intelligence
Techniques to Building Sea-Going Ships," Reports of the
Machine Learning and Inference Laboratory, MLI 90-6, School
of Information Technology and Engineering, George Mason
University, March 1990.
P90-9
Freeman, R., "PRODIGY: Its Exploration and Use," Reports
of the Machine Learning and Inference Laboratory, MLI 90-7,
School of Information Technology and Engineering, George Mason
University, May 1990.
P90-10
Michalski, R.S. and Kodratoff, Y., "Research in Machine
Learning; Recent Progress, Classification of Methods and Future
Directions," Kodratoff , Y. and Michalski, R.S. (eds.), Machine
Learning: An Artificial Intelligence Approach, Vol. III, San
Mateo, CA, Morgan Kaufmann Publishers, pp. 3-30, June 1990.
P90-11
Michalski, R.S., "Learning Flexible Concepts: Fundamental
Ideas and a Method Based on Two-tiered Representation,"
Kodratoff , Y. and Michalski, R.S. (eds.), Machine Learning:
An Artificial Intelligence Approach, Vol. III, San Mateo, CA,
Morgan Kaufmann Publishers, pp. 63-111, June 1990.
P90-12
Falkenhainer, B.C. and Michalski, R.S., "Integrating
Quantitative and Qualitative Discovery in the ABACUS System,"
Kodratoff , Y. and Michalski, R.S. (eds.), Machine Learning:
An Artificial Intelligence Approach, Vol. III, San Mateo, CA,
Morgan Kaufmann Publishers, pp. 153-190, June 1990.
P90-13
De Jong, K.A., "Genetic Algorithm Based Learning,"
Kodratoff , Y. and Michalski, R.S. (eds.), Machine Learning:
An Artificial Intelligence Approach, Vol. III, San Mateo, CA,
Morgan Kaufmann Publishers, pp. 611-638, June 1990.
P90-14
Kodratoff, Y., "Learning Expert Knowledge by Improving the
Explanations Provided by the System," Kodratoff , Y. and
Michalski, R.S. (eds.), Machine Learning: An Artificial
Intelligence Approach, Vol. III, San Mateo, CA, Morgan
Kaufmann Publishers, pp. 433-473, June 1990.
P90-15
Tecuci, G. and Kodratoff, Y., "Apprenticeship Learning in
Imperfect Domain Theories," Kodratoff , Y. and Michalski, R.S.
(eds.), Machine Learning: An Artificial Intelligence Approach,
Vol. III, San Mateo, CA, Morgan Kaufmann Publishers, pp. 514-552,
June, 1990.
P90-16
Stefanski, P.A., Wnek, J. and Zhang, J., "Bibliography of
Recent Machine Learning Research 1985-1989," Kodratoff , Y.
and Michalski, R.S. (eds.), Machine Learning: An Artificial
Intelligence Approach, Vol. III, San Mateo, CA, Morgan
Kaufmann Publishers, pp. 685-789, June 1990.
P90-17
Kodratoff, Y. and Michalski, R.S. (Eds.), Machine Learning: An
Artificial Intelligence Approach, Vol. III, San Mateo, CA,
Morgan Kaufmann Publishers, June 1990.
P90-18
De Jong K.A. and Spears, W., "An Analysis of Multipoint
Crossover for Genetic Algorithms," Proceedings of FOCA-90,
June 1990.
P90-19
Pachowicz, P.W., "Integrating Low Level Features Computation
with Inductive Learning Techniques for Texture Recognition,"
International Journal of Pattern Recognition and Artificial
Intelligence, Vol. 4, No.2, pp. 147-165, June 1990.
P90-20
Bala, J.W. and Pachowicz, P.W., "Recognizing Noisy Patterns
of Texture Via Iterative Optimization and Matching of Their Rule
Description," Reports of the Machine Learning and
Inference Laboratory, MLI 90-8, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
June 1990.
P90-21
Pachowicz, P.W., "Learning-Based Architecture for the Robust
Recognition of Variable Texture to Navigate in Natural Terrain,"
Proceedings IEEE International Workshop on Intelligent Robots
and Systems, '90, Japan, pp. 135-142, July 1990.
P90-22
Bala, J.W., "Combining Structural and Statistical Features
in a Machine Learning Technique for Texture Classification,"
Proceedings of the Third International Conference on
Industrial and Engineering Applications of AI and Expert Systems,
July 1990.
P90-23
Michalski, R.S., Dontas, K. and Boehm-Davis, D., "Plausible
reasoning: An outline of theory and experiments to validate its
structural aspects," Reports of the Machine Learning and
Inference Laboratory, MLI 90-9, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
1990.
P90-24
Sibley, E.H., Michael, J.B. and Wexelblat, R.L., "Policy
Management, Economics and Risk," Proceedings of the IFAC
Second International Conference on Economics and Artificial
Intelligence, Paris, France, July 1990.
P90-25
De Jong, K.A., "Using Genetic Algorithms for Symbolic
Learning Tasks," Proceedings of the Conference on the
Simulation of Adaptive Behavior, Paris, France, September
1990.
P90-26
Wechsler, H., Computational Vision, New York: Academic
Press, September 1990.
P90-27
Bergadano, F., Matwin, S., Michalski, R.S. and Zhang, J., "Learning
Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System,"
Reports of the Machine Learning and Inference Laboratory,
MLI 90-10, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, September 1990.
P90-28
Zhang, J., "Learning Flexible Concepts from Examples:
Employing the Ideas of Two-Tiered Concept Representation," Reports
of the Machine Learning and Inference Laboratory, MLI 90-11,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, September 1990.
P90-29
De Jong, K.A. and Spears, W.A., "An Analysis of the
Interacting Roles of Population Size and Crossover in Genetic
Algorithms," Conference on Parallel Problem Solving from
Nature, Dortmund, Germany, October 1990.
P90-30
Wnek, J., Sarma, J., Wahab, A. and Michalski, R.S., "Comparing
Learning Paradigms via Diagrammatic Visualization: A Case Study
in Concept Learning Using Symbolic, Neural Net and Genetic
Algorithm Methods," Proceedings of the 5th International
Symposium on Methodologies for Intelligent Systems, ISMIS'90,
Knoxville, TN, pp. 428-437, October 1990.
P90-31
Michalski, R.S., "A Methodological Framework for
Multistrategy Cooperative Learning," Proceedings of the 5th
International Symposium on Methodologies for Intelligent Systems,
ISMIS'90, Knoxville, TN, pp. 404-411, October 1990.
P90-32
De Jong, K.A., "Using Genetic Algorithms as a Heuristic for
NP-Complete Problems," Proceedings of the ORSA/JIMM
Conference, New York, October 1990.
P90-33
Spears, W.M. and De Jong, K.A., "Using Genetic Algorithms
for Supervised Concept Learning," Proceedings of the
Tools for AI Conference, Reston, VA, November 1990.
P90-34
Bala, J.W. and De Jong, K.A., "Generation of Feature
Detectors for Texture Discrimination by Genetic Search," Proceedings
of the Tools for AI Conference, Reston, VA, November 1990.
P90-35
Dontas, K. and De Jong, K.A., "Discovery of Maximal Distance
Codes Using Genetic Algorithms," Proceedings of the Tools
for AI Conference, Reston, VA, November 1990.
P90-36
Tecuci, G., "A Multistrategy Learning Approach To Domain
Modeling and Knowledge Acquisition," Reports of the
Machine Learning and Inference Laboratory, MLI 90-12, School
of Information Technology and Engineering, George Mason
University, Fairfax, VA, November 1990.
P90-37
Char, J.M., Cherkassky, V. and Wechsler, H., "Fault-Tolerant
Database Using Distributed Associative Memories," Information
Sciences, 1990.
P90-38
Wechsler, H. (Ed.), Neural Networks for Visual and Machine
Perception, Oxford University Press, 1990.
P90-39
Kaufman, K., Schultz, A. and Michalski, R.S., "EMERALD 1: An
Integrated System of Machine Learning and Discovery Programs for
Education and Research: Programmer's Guide for the Sun
Workstation," Reports of the Machine Learning and
Inference Laboratory, MLI 90-13, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
December 1990.
P90-40
Kodratoff, Y., Rouveirol, C., Tecuci, G. and Duval, B., "Symbolic
Approaches to Uncertainty," INTELLIGENT SYSTEMS: State of
the Art and Future Directions, Z.W. Ras and M. Zemankova (Eds),
1990.
P90-41
Kaufman, K. and Michalski, R.S., "EMERALD 1: An Integrated
System of Machine Learning and Discovery Programs for Education
and Research: Programmer's Guide for the VaxStation," Reports
of the Machine Learning and Inference Laboratory, MLI 90-14,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, December 1990.
P90-42
Michalski, R.S., "Multistrategy Constructive Learning:
Toward a Unified Learning Theory," invited paper at the ONR
Knowledge Acquisition Workshop, Crystal City, VA, November 6-7
1990.
P90-43
Michalski, R.S., "Theory and Methodology of Inductive
Learning," in Dieterrich, T. and Shavlik, J. (eds.), Readings
in Machine Learning, Morgan Kaufmann, 1990.
P91-1
Michalski, R.S., "Searching for Knowledge in a World Flooded
with Facts," Applied Stochastic Models and Data Analysis,
Vol. 7, pp. 153-163, January, 1991.
P91-2
Bala, J.W. and Pachowicz, P.W., "Application of Symbolic
Machine Learning to the Recognition of Texture Concepts," Proceedings
of the 7th IEEE Conference on Artificial Intelligence Application,
Miami, FL, February 1991.
P91-3
Pachowicz, P.W. and Bala, J.W. "Optimization of Concept
Prototypes for the Recognition of Noisy Texture Data," Reports
of the Machine Learning and Inference Laboratory, MLI 91-1,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, April 1991.
P91-4
Pachowicz, P.W., "Learning Invariant Texture Characteristics
to Dynamic Environments: A Model Evolution," Reports of
the Machine Learning and Inference Laboratory, MLI 91-2,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, April 1991.
P91-5
Tecuci, G., "A Multistrategy Learning Approach to Domain
Modeling and Knowledge Acquisition," in Kodratoff, Y. (ed.),
Proceedings of the European Conference on Machine Learning,
Porto, Portugal, Springer-Verlag, 1991.
P91-6
Tecuci, G. and Michalski, R.S., "Input Understanding as a
Basis for Multistrategy Task-adaptive Learning," in Ras, Z.
and Zemankova, M. (eds.), Proceedings of the International
Symposium on Methodologies for Intelligent Systems, Lecture Notes
on Artificial Intelligence, Springer-Verlag, 1991.
P91-7
Michalski, R.S., "Searching for Knowledge in
a World Flooded with Facts," an invited talk, Proceedings of
the Fifth International Symposium on Applied Stochastic Models and Data
Analysis, Granada, Spain, April 23-26, 1991.
P91-8
Kerschberg, L. and Weishar D., "An Intelligent Heterogeneous
Autonomous Database Architecture for Semantic Heterogeneity
Support," IEEE Workshop on Interoperability in
Multidatabase Systems, Kyoto, Japan, April 1991.
P91-9
Bergadano, F., Matwin, S., Michalski R.S. and Zhang, J., "Learning
Two-tiered Descriptions of Flexible Concepts: The POSEIDON System,"
Reports of the Machine Learning and Inference Laboratory,
MLI 91-3, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, May 1991.
P91-10
Wnek, J. and Michalski, R.S., "Hypothesis-Driven
Constructive Induction in AQ17: A Method and Experiments," Reports
of the Machine Learning and Inference Laboratory, MLI 91-4,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, May 1991.
P91-11
Tecuci, G. and Michalski, R.S., "A Method for Multistrategy
Task-adaptive Learning Based on Plausible Justifications,"
Birnbaum, L. and Collins, G. (eds.), Machine Learning:
Proceedings of the Eighth International Workshop, San Mateo,
CA, Morgan Kaufmann, June 1991.
P91-12
Pachowicz, P.W. and Bala, J.W., "Optimization of Concept
Prototypes for the Recognition of Noisy Texture Data,"
Birnbaum, L. and Collins, G. (eds.), Machine Learning:
Proceedings of the Eighth International Workshop, San Mateo,
CA, Morgan Kaufmann, June 1991.
P91-13
Michalski, R.S., "Toward a Unified Theory of
Learning: An Outline of Basic Ideas," Invited paper, First World
Conference on the Fundamentals of Artificial Intelligence,
Paris, France, July 1-5, 1991.
P91-14
Pachowicz, P.W. and Bala, J.W., "Texture Recognition Through
Machine Learning and Concept Optimization," Reports of
the Machine Learning and Inference Laboratory, MLI 91-5,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, July 1991.
P91-15
Tecuci, G., "Steps Toward Automating Knowledge Acquisition
for Expert Systems," in Rappaport, A., Gaines, B. and Boose,
J. (eds.), Proceedings of the AAAI-91 Workshop on Knowledge
Acquisition "From Science To Technology to Tools,"
Anaheim, CA, July 1991.
P91-16
Kaufman, K., Michalski, R.S. and Kerschberg, L., "An
Architecture for Integrating Machine Learning and Discovery
Programs into a Data Analysis System," Proceedings of the
AAAI-91 Workshop on Knowledge Discovery in Databases, Anaheim,
CA, July 1991.
P91-17
Spears, W.M. and De Jong, K.A., "An Analysis of Multi-point
Crossover," in Rawlins, G.J.E. (ed.), Foundations of
Genetic Algorithms, Morgan Kaufmann, San Mateo, July 1991.
P91-18
Spears, W.M. and De Jong, K.A., "On the Virtues of
Parameterized Uniform Crossover," Proceedings of the 4th
International Conference on Genetic Algorithms, Morgan
Kaufmann, July 1991.
P91-19
Wnek, J. and Michalski, R.S., "Hypothesis-Driven
Constructive Induction in AQ17: A Method and Experiments," Proceedings
of the IJCAI-91 Workshop on Evaluating and Changing
Representation in Machine Learning, Sydney, Australia, August
1991.
P91-20
De Jong, K.A. and Spears, W.M., "Learning Concept
Classification Rules Using Genetic Algorithms," Proceedings
of IJCAI-91, Morgan Kaufmann, Sydney, Australia, August 1991.
P91-21
Kerschberg, L. and Seligman, L., "Federated Knowledge and
Database Systems: A New Architecture for Integrating of AI and
Database Systems," Proceedings of the IJCAI-91 Workshop
on Integrating Artificial Intelligence and Databases, Sydney,
Australia, August 1991.
P91-22
Michalski, R.S., "Beyond Prototypes and Frames: The Two-tiered
Concept Representation," Reports of the Machine Learning
and Inference Laboratory, MLI 91-6, School of Information
Technology and Engineering, George Mason University, September
1991.
P91-23
Tecuci, G., "Automating Knowledge Acquisition As Extending,
Updating, and Improving A Knowledge Base," Reports of the
Machine Learning and Inference Laboratory, MLI 91-7, School
of Information Technology and Engineering, George Mason
University, Fairfax, VA, September 1991.
P91-24
Michael, J., "Validation, Verification, and Experimentation
with Abacus2," Reports of the Machine Learning and
Inference Laboratory, MLI 91-8, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
September 1991.
P91-25
Bala, J., De Jong, K.A. and Pachowicz, P., "Using Genetic
Algorithms to Improve the Performance of Classification Rules
Produced by Symbolic Inductive Method," Proceedings of
the Sixth International Symposium on Methodologies for
Intelligent Systems, ISMIS'91, Charlotte, North Carolina,
October 16-19, 1991.
P91-26
Kaufman, K., Michalski, R.S. and Kerschberg, L., "Knowledge
Extraction from Databases: Design Principles of the INLEN System,"
Proceedings of the Sixth International Symposium on
Methodologies for Intelligent Systems, ISMIS '91, October 16-19,
1991.
P91-27
Michalski, R.S., "Inferential Learning Theory: A Conceptual
Framework for Characterizing Learning Processes," Reports
of the Machine Learning and Inference Laboratory, MLI 91-9,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, October 1991.
P91-28
Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B.,
Cheng, J., De Jong, K.A., Dzeroski, S., Fahlman, S.E., Hamann, R.,
Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski,
R.S., Mitchell, T., Pachowicz, P., Vafaie, H., Van de Velde,W.,
Wenzel, W., Wnek, J. and Zhang, J., "The MONK's problems: A
Performance Comparison of Different Learning Algorithms,"
Carnegie Mellon University, Pittsburgh, PA, October 1991.
P91-29
Kerschberg, L. and Baum, R., "A Taxonomy of Knowledge-Based
Approaches to Fault Management for Telecommunications Networks,"
IEEE Conference on Systems, Man and Cybernetics,
Charlottesville, VA, October 1991.
P91-30
Pachowicz, P., "Recognizing and Incrementally Evolving
Texture Concepts in Dynamic Environments: An Incremental Model
Generalization Approach," Reports of the Machine Learning
and Inference Laboratory, MLI 91-10, School of Information
Technology and Engineering, George Mason, University, Fairfax, VA,
November 1991.
P91-31
Michalski, R.S. and Tecuci, G. (Eds.), Proceedings of the
First International Workshop on Multistrategy Learning, MSL-91,
School of Information Technology and Engineering, George Mason
University, Harpers Ferry, WV, November 7-9, 1991.
P91-32
Michalski, R.S., "Inferential Learning Theory: A Conceptual
Framework for Characterizing Learning Processes," Proceedings
of the First International Workshop on Multistrategy Learning,
MSL-91, Harpers Ferry, WV, November 7-9, 1991.
P91-33
Wnek, J. and Michalski, R.S., "An Experimental Comparison of
Symbolic and Subsymbolic Learning Paradigms: Phase I -- Learning
Logic-Style Concepts," Proceedings of the First
International Workshop on Multistrategy Learning, MSL-91,
Harpers Ferry, WV, November 7-9, 1991.
P91-34
Vafaie, H. and De Jong, K.A., "Improving the Performance of
a Rule Induction System Using Genetic Algorithm," Proceedings
of the First International Workshop on Multistrategy Learning,
MSL-91, Harpers Ferry, WV, November 7-9, 1991.
P91-35
Bala, J., De Jong, K.A. and Pachowicz, P., "Integration of
Inductive Learning and Genetic Algorithms to Learn Optimal
Concept Descriptions from Engineering Data," Proceedings
of the First International Workshop on Multistrategy Learning,
MSL-91, Harpers Ferry, WV, November 7-9, 1991.
P91-36
Tecuci, G., "Learning as Understanding the External World,"
Proceedings of the First International Workshop on
Multistrategy Learning, MSL-91, Harpers Ferry, WV, November 7-9,
1991.
P91-37
Bloedorn, E. and Michalski, R.S., "Data
Driven Constructive Induction in AQ17-PRE: A Method and
Experiments," Proceedings of the Third International
Conference on Tools for AI, San Jose, CA, November 9-14, 1991.
P91-38
Bala, J. and Michalski, R.S., "Learning Texture Concepts
Through Multilevel Symbolic Transformations," Proceedings
of the Third International Conference on Tools for Artificial
Intelligence, San Jose, CA, November 9-14, 1991.
P91-39
Janssen, T., Bloedorn, E., Hieb, M.R. and Michalski, R.S., "Learning
Rules for Preventing and Diagnosing Faults in Large-Scale Data
Communications Networks: An Exploratory Study," Proceedings
of the Fourth International Symposium on Artificial Intelligence,
Cancun, Mexico, November 13-15. 1991.
P91-40
Pachowicz, P.W., "Application of Symbolic Inductive Learning
to the Acquisitions and Recognition of Noisy Texture Concepts,"
in Applications of Learning and Planning Methods, November
1991.
P91-41
Gomaa, H., Kerschberg, L., Bosch, C., Sugumaran, V. and Tavakoli,
I., "A Prototype Software Engineering Environment for Domain
Modeling and Reuse," NASA/Goddard Sixteenth Annual
Software Engineering Workshop, December 4-5, 1991.
P91-42
Weishar, D. and Kerschberg, L., "Data/Knowledge Packets as a
Means of Supporting Semantic Heterogeneity in Multidatabase
Systems," ACM SIGMOD Record, December 1991.
P91-43
Kaufman, K.A., Michalski, R.S. and Kerschberg, L., "Mining
for Knowledge in Databases: Goals and General Description of the
INLEN System," in Piatetski-Shapiro, G. and Frawley, W.J. (eds.),
Knowledge Discovery in Databases, AAAI Press/The MIT Press,
Menlo Park, CA 1991.
P91-44
Hamburger, H. and Maney, T. "Twofold Continuity in Language
Learning," Computer-Assisted Language Learning, Vol.
4, No. 2, pp. 8l-92, 1991.
P91-45
Pachowicz, P.W. "Local Characteristics of Binary Images and
Their Application to the AutomaticControl of Low-Level Robot
Vision," Computer Vision, Graphics and Image Processing,
Academic Press, 1991.
P91-46
Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B.,
Cheng, J., De Jong, K.A., Dzeroski, S., Fahlman, S.E., Hamann, R.,
Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski,
R.S., Mitchell, T., Pachowicz, P., Vafaie, H., Van de Velde, W.,
Wenzel, W., Wnek, J. and Zhang, J., "The MONK's problems: A
Performance Comparison of Different Learning Algorithms," Computer
Science Reports, CMU-CS-91-197, Carnegie Mellon University (Revised
version), Pittsburgh, PA, December 1991.
P91-47
Michalski, R.S., Kaufman, K. and Wnek, J., "The AQ Family of
Learning Programs: A Review of Recent Developments and an
Exemplary Application," Reports of the Machine Learning
and Inference Laboratory, MLI 91-11, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
December 1991.
P91-48
Bloedorn, E. and Michalski, R.S., "Constructive
Induction from Data in AQ17-DCI: Further Experiments," Reports
of the Machine Learning and Inference Laboratory, MLI 91-12,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, December 1991.
P92-1
Bergadano, F., Matwin, S., Michalski R. S. and Zhang, J., "Learning
Two-tiered Descriptions of Flexible Concepts: The POSEIDON System,"
Machine Learning, Vol. 8, No. 1, pp. 5-43, January 1992.
P92-2
Wnek, J., "Version Space Transformation with Constructive
Induction: The VS* Algorithm," Reports of the Machine
Learning and Inference Laboratory, MLI 92-1, George Mason
University, Fairfax, VA, January 1992.
P92-3
Wnek, J. and Michalski, R.S., "Hypothesis-driven
Constructive Induction in AQ17: A Method and Experiments," Reports
of the Machine Learning and Inference Laboratory, MLI 92-2,
George Mason University, Fairfax, VA, January 1992.
P92-4
De Jong, K.A. and Spears, W.M., "A Formal Analysis of the
Role of Multi-point Crossover in Genetic Algorithms," Annals
of Mathematics and Artificial Intelligence, Vol. 5, No. 1,
January 1992.
P92-5
Fermanian, T. and Michalski, R.S., "AgriAssistant: A New
Generation Tool for Developing Agricultural Advisory Systems,"
in Mann, C.K. and Ruth, S.R. (eds.), Expert Systems in the
Developing Countries: Practice and Promise, Westview Press
Publication, 1992.
P92-6
Michalski, R.S., "Knowledge Acquisition by Encoding Expert
Rules versus Computer Induction from Examples: A Case Study
Involving Soybean Pathology," in Partridge (ed.), Artificial
Intelligence and Software Engineering, Alex Publishing
Corporation, 1992.
P92-7
Michalski, R.S., Kerschberg, L., Kaufman, K.A. and Ribeiro, J.S.,
"Searching for Knowledge in Large Databases," Proceedings
of the First International Conference on Expert Systems and
Development, Cairo Egypt, April 1992.
P92-8
Bala, J., Michalski, R.S. and Wnek, J., "The Principal Axes
Method for Constructive Induction," Proceedings of the 9th
International Conference on Machine Learning, D. Sleeman and
P. Edwards (Eds.), Aberdeen, Scotland, July 1992.
P92-9
Tecuci G., "Cooperation in Knowledge Base Refinement,"
Sleeman, D. and Edwards, P. (eds.), Proceedings of the Ninth
International Machine Learning Conference (ML92), Morgan
Kaufmann, Aberdeen, Scotland, July 1992.
P92-10
Tecuci G. and Hieb M.R., "Consistency Driven Knowledge
Elicitation Within a Learning Oriented Representation of
Knowledge," Proceedings of the AAAI-92 Workshop on
Knowledge Representation Aspects of Knowledge Acquisition,
Los Angeles, CA, July 1992.
P92-11
De Jong, K.A. and Sarma, J., "Generation Gaps Revisited,"
Proceedings of the Second Workshop on Foundations of Genetic
Algorithms, Morgan Kaufmann, July 1992.
P92-12
De Jong, K.A., "Genetic Algorithms are NOT Function
Optimizers," Proceedings of the Second Workshop on
Foundations of Genetic Algorithms, Morgan Kaufmann, July 1992.
P92-13
Michalski, R.S., Kerschberg, L., Kaufman, K.A. and Ribeiro, J.S.,
"Mining For Knowledge in Databases: The INLEN Architecture,
Initial Implementation and First Results," Intelligent
Information Systems: Integrating Artificial Intelligence and
Database Technologies, Vol. 1, No. 1, pp. 85-113, August 1992.
P92-14
Kulpa, Z. and Sobolewski, M., "Knowledge-directed Graphical
and Natural Language Interface with a Knowledge-based Concurrent
Engineering Environment," Proceedings of the 8th
International Conference on CAD/CAM, Robotics and Factories of
the Future, Metz, France, August 1992.
P92-15
Pachowicz, P.W., Bala, J. and Zhang, J., "Methodology for
Iterative Noise-Tolerant Learning and Its Application to Object
Recognition in Computer Vision," Proceedings of the 6th
International Conference on Systems Research, Informatics and
Cybernetics, Baden-Baden, Germany, August 1992.
P92-16
Pachowicz, P.W., Hieb, M.R. and Mohta, P., "A Learning-Based
Incremental Model Evolution for Invariant Object Recognition,"
Proceedings of the 6th International Conference on Systems
Research, Informatics and Cybernetics, Baden-Baden, Germany,
August 1992.
P92-17
Tecuci G., "Automating Knowledge Acquisition as Extending,
Updating and Improving a Knowledge Base," IEEE
Transactions on Systems, Man, and Cybernetics, Vol. 22, No. 6,
pp. 1444-1460, November/December 1992.
P92-18
Michalski, R.S., "Inferential Theory of Learning: Developing
Foundations for Multistrategy Learning," Reports of the
Machine Learning and Inference Laboratory, MLI 92-3, George
Mason University, Fairfax, VA, September 1992.
(click here for an extended version)
P92-19
Wnek, J. and Michalski, R.S., "Comparing Symbolic and
Subsymbolic Learning: Three Studies," Reports of the
Machine Learning and Inference Laboratory, MLI 92-4, George
Mason University, Fairfax, VA, September 1992.
P92-20
De Jong, K. A., "Are Genetic Algorithms Function Optimizers?"
Proceedings of PPSN-92, the 2nd Conference on Parallel Problem
Solving from Nature, Brussels, Belgium, Elsevier-Holland,
September 1992.
P92-21
Gomaa, H., Kerschberg, L. and Sugumaran, V., "A Knowledge-Based
Approach to Generating Target Systems Specifications from a
Domain Model," Proceedings of IFIP World Computer
Congress, Madrid, Spain, September 1992.
P92-22
Vamos, T., "Epistemology, Uncertainty and Social Change,"
Reports of the Machine Learning and Inference Laboratory,
MLI 92-5, George Mason University, Fairfax, VA, October 1992.
P92-23
Pachowicz, P.W., "A Learning-Based Evolution of Concept
Descriptions for an Adaptive Object Recognition," Proceedings
of the 4th International Conference on Tools with Artificial
Intelligence, Arlington, VA, pp. 316-323, November 1992.
P92-24
Pachowicz, P.W., Bala, J. and Zhang, J., "Iterative Rule
Simplification for Noise Tolerant Inductive Learning," Proceedings
of the 4th International Conference on Tools with Artificial
Intelligence, Arlington, VA, pp. 452-453, November 1992.
P92-25
Vafaie, H. and De Jong, K.A., "Genetic Algorithms as a Tool
for Feature Selection in Machine Learning," Proceedings
of the 4th International Conference on Tools with Artificial
Intelligence, Arlington, VA, November 1992.
P92-26
Seligman, L. and Kerschberg, L., "Approximate Knowledge Base/Database
Consistency: An Active Database Approach," Proceedings of
the First International Conference on Information and Knowledge
Management, Baltimore, MD, November 1992.
P92-27
Yoon, J.P. and Kerschberg, L., "A Framework for Constraint
Management in Object-Oriented Databases," Proceedings of
the First International Conference on Information and Knowledge
Management, Baltimore, MD, November 1992.
P92-28
Crain, S. and Hamburger, H., "Semantics, Knowledge and NP
Modification," in Levine, R. (ed.), Formal Grammar:
Theory and Implementation, Oxford University Press, Oxford,
England, 1992.
P92-29
Hamburger, H. and Hashim, R., "Foreign Language Tutoring and
Learning Environment," in Swartz, M. and Yazdani, M. (eds.),
Intelligent Tutoring Systems for Foreign Language Learning,
Springer Verlag, New York & Berlin, 1992.
P92-30
Hamburger, H. and Lodgher, A., "Semantically Constrained
Exploration and Heuristic Guidance," in Psotka, J. and Farr,
M. (eds.), Intelligent Instruction by Computer, Taylor and
Francis, New York, 1992.
P92-31
Hashim, R. and Hamburger, H., "Discourse Style and Situation
Viewpoint for a Conversational Language Tutor," Proceedings
of the International Conference on Computer-Assisted Learning,
Wolfville, Nova Scotia, Canada, Springer-Verlag, New York, 1992.
P92-32
Pan, J. and Hamburger, H., "A Knowledge-based Learning
System for Chinese Character Writing," Proceedings of the
International Conference on Computer Processing of Chinese and
Oriental Languages, Clearwater Beach, FL, December 15-19,
1992.
P92-33
Hieb, M.R. and Tecuci, G., "Two Methods for Consistency-driven
Knowledge Elicitation," Reports of the Machine Learning
and Inference Laboratory, MLI 92-6, George Mason University,
Fairfax, VA, December 1992.
P92-34
Arciszewski, T., Bloedorn, E., Michalski, R.S., Mustafa, M. and
Wnek, J., "Constructive Induction in Structural Design,"
Reports of the Machine Learning and Inference Laboratory,
MLI 92-7, George Mason University, Fairfax, VA, December 1992.
P92-35
Tecuci G. and Hieb M.R., "Consistency-driven Knowledge
Elicitation: Using a Machine Learning Oriented Knowledge
Representation to Integrate Learning and Knowledge Elicitation in
NeoDISCIPLE," Reports of the Machine Learning and
Inference Laboratory, MLI 92-8, George Mason University,
Fairfax, VA, December 1992.
P92-36
Arciszewski, T., Dybala, T. and Wnek, J., "A Method for
Evaluation of Learning Systems," HEURISTICS, The Journal
of Knowledge Engineering, Special Issue on Knowledge
Acquisition and Machine Learning, Vol. 5, No. 4, pp. 22-31, 1992.
P92-37
Hieb, M.R., Silverman, B.G. and Mezher, T.M., "Rule
Acquisition for Dynamic Engineering Domains," HEURISTICS,
The Journal of Knowledge Engineering, Special Issue on
Knowledge Acquisition and Machine Learning, Vol. 5, No. 4, pp. 72-82,
1992.
P92-38
Wnek, J. and Michalski, R.S., "Experimental Comparison of
Symbolic and Subsymbolic Learning," HEURISTICS, The
Journal of Knowledge Engineering, Special Issue on Knowledge
Acquisition and Machine Learning, Vol. 5, No. 4, pp. 1-21, 1992.
P92-39
Bala J.W. and Pachowicz P., "Recognizing Noisy Pattern Via
Iterative Optimization and Matching of Their Rule Description,"
International Journal on Pattern Recognition and Artificial
Intelligence, Vol. 6, No. 4, 1992.
P92-40
Michalski, R.S., "LEARNING = INFERENCING + MEMORIZING: Basic
Concepts of Inferential Theory of Learning and Their Use for
Classifying Learning Processes," in Chipman, S. and
Meyrowitz, A. (eds.), Cognitive Models of Learning, 1992.
P92-41
Bala J., K., Bloedorn, E., De Jong, Kaufman, K., Michalski, R.S.,
Pachowicz P., Vafaie, H., Wnek, J. and Zhang, J., "A Brief
Review of AQ Learning Programs and Their Application to the MONKS'
Problems," Reports of the Machine Learning and Inference
Laboratory, MLI 92-9, George Mason University, Fairfax, VA,
1992.
P93-1
Michalski, R.S., "Toward a Unified Theory of Learning:
Multistrategy Task-adaptive Learning," in Buchanan, B.G. and
Wikins, D.C. (eds.), Readings in Knowledge Acquisition and
Learning: Automating the Construction and Improvement of Expert
Systems, Morgan Kaufmann, San Mateo, 1993.
P93-2
Michalski, R.S., "A Theory and Methodology of Inductive
Learning," in Buchanan, B.G. and Wikins, D.C. (eds.), Readings
in Knowledge Acquisition and Learning: Automating the
Construction and Improvement of Expert Systems, Morgan
Kaufmann, San Mateo, 1993.
P93-3
Van Mechelen, I., Hampton, J., Michalski, R.S. and Theuns, P. (Eds.),
Categories and Concepts: Theoretical Views and Inductive Data
Analysis, Academic Press, New York, 1993.
P93-4
Michalski, R.S., "Beyond Prototypes and Frames: The Two-tiered
Concept Representation," in Van Mechelen, I., Hampton, J.,
Michalski, R.S. and Theuns, P. (eds.), Categories and Concepts:
Theoretical Views and Inductive Data Analysis, Academic Press,
New York, 1993.
P93-5
Michalski, R.S., "Learning = Inferencing + Memorizing:
Introduction to Inferential Theory of Learning," in Chipman,
S. and Meyrowitz, A. (eds.), Foundations of Knowledge
Acquisition, Vol. 2: Machine Learning, 1993.
P93-6
Michalski, R.S., Bergadano, F., Matwin, S. and Zhang, J., "Learning
Flexible Concepts Using a Two-tiered Representation," in
Chipman, S. and Meyrowitz, A. (eds.), Foundations of Knowledge
Acquisition, Vol. 2: Machine Learning, 1993.
P93-7
Michalski, R.S., Pachowicz, P.W., Rosenfeld, A. and Aloimonos, Y.,
"Machine Learning and Vision: Research Issues and Promising
Directions," NSF/DARPA Workshop on Machine Learning and
Vision (MLV-92), HarpersFerry, WV, October 15-17, 1992; Reports
of the Machine Learning and Inference Laboratory, MLI 93-1,
School of Information Technology and Engineering, George Mason
University, February 1993.
P93-8
Wnek, J., "Hypothesis-driven Constructive Induction,"
Ph.D. dissertation, School of Information Technology and
Engineering; Reports of the Machine Learning and Inference
Laboratory, MLI 93-2, School of Information Technology and
Engineering, George Mason University, March 1993.
P93-9
Bala, J.W., "Learning to Recognize Visual Concepts:
Development and Implementation of a Method for Texture Concept
Acquisition Through Inductive Learning," Ph.D. dissertation;
School of Information Technology and Engineering, Reports of
the Machine Learning and Inference Laboratory, MLI 93-3,
School of Information Technology and Engineering, George Mason
University, March 1993.
P93-10
Michalski, R.S., Bala, J.W. and Pachowicz, P.W., "GMU
Research on Learning in Vision: Initial Results," Proceedings
of the DARPA Image Understanding Workshop, Washington D.C.,
April 18-21, 1993.
P93-11
Bloedorn, E., Wnek, J. and Michalski, R.S., "Multistrategy
Constructive Induction," Reports of the Machine Learning
and Inference Laboratory, MLI 93-4, School of Information
Technology and Engineering, George Mason University, May 1993.
P93-12
Hieb, M. and Michalski, R.S., "Knowledge Representation
Based on Dynamically Interlaced Hierarchies: Basic Ideas and
Examples," Reports of the Machine Learning and Inference
Laboratory, MLI 93-5, School of Information Technology and
Engineering, George Mason University, May 1993.
P93-13
Bloedorn, E., Wnek, J. and Michalski, R.S., "Multistrategy
Constructive Induction: AQ17-MCI," Proceedings of the
Second International Workshop on Multistrategy Learning (MSL93),
Harpers Ferry, WV, Morgan Kaufmann, pp. 188-203, May 27-29, 1993.
P93-14
Hieb, M. and Michalski, R.S., "Knowledge Representation for
Multistrategy Task-adaptive Learning: Dynamic Interlaced
Hierarchies," Proceedings of the Second International
Workshop on Multistrategy Learning (MSL93), Harpers Ferry, WV,
Morgan Kaufmann, May 27-29, 1993.
P93-15
Michalski, R.S. and Tecuci, G. (Eds.), Proceedings of the
Second International Workshop on Multistrategy Learning (MSL93),
Harpers Ferry, WV, Morgan Kaufmann, May 27-29, 1993.
P93-16
Imam, I.F. and Michalski, R.S., "Learning Decision Trees
from Decision Rules: A Method and Initial Results from a
Comparative Study," Reports of the Machine Learning and
Inference Laboratory, MLI 93-6, School of Information
Technology and Engineering, George Mason University, May 1993.
P93-17
Wnek, J., Michalski, R.S. and Arciszewski, T., "An
Application of Constructive Induction to Engineering Design,"
Reports of the Machine Learning and Inference Laboratory,
MLI 93-7, School of Information Technology and Engineering,
George Mason University, May 1993.
P93-18
Imam, I.F. and Michalski, R.S., "Should Decision Trees Be
Learned from Examples or from Decision Rules?" Lecture
Notes in Artificial Intelligence, Springer Verlag; Proceedings
of the 7th International Symposium on Methodologies for
Intelligent Systems, ISMIS, Trondheim, Norway, June 15-18,
1993.
P93-19
Imam, I.F., Michalski, R.S. and Kerschberg, L., "Discovering
Attribute Dependence in Databases by Integrating Symbolic
Learning and Statistical Analysis Techniques," Proceedings
of the AAAI-93 Workshop on Knowledge Discovery in Databases,
Washington, D.C., July 11-12, 1993.
P93-20
Michalski, R.S. and Tecuci, G., "Multistrategy Learning,"
Tutorial at the National Conference on Artificial Intelligence,
AAAI-93, Washington D.C., July 11-12, 1993.
P93-21
Michalski, R.S., "Inferential Theory of Learning as a
Conceptual Basis for Multistrategy Learning," Machine
Learning, Special Issue on Multistrategy Learning, Vol. 11,
pp. 111-151, 1993.
P93-22
Wnek, J., Michalski, R.S. and Arciszewski, T., "An
Application of Constructive Induction to Engineering Design,"
Proceedings of the IJCAI-93 Workshop on AI in Design,
Chambery France, August 1993.
P93-23
Michalski, R.S. and Tecuci, G., "Multistrategy Learning,"
Tutorial at the International Joint Conference on Artificial
Intelligence, IJCAI-93, Chambery, France, August 1993.
P93-24
Kaufman, K.A., Schultz, A. and Michalski, R.S., "EMERALD 2:
An Integrated System of Machine Learning and Discovery Programs
for Education and Research, User's Guide," Reports of the
Machine Learning and Inference Laboratory, MLI 93-8, School
of Information Technology and Engineering, George Mason
University, Fairfax, VA, September 1993.
P93-25
Kaufman, K.A., Michalski, R.S. and Schultz, A., "EMERALD 2:
An Integrated System of Machine Learning and Discovery Programs
for Education and Research, Programmer's Guide for the SUN
Workstation," Reports of the Machine Learning and
Inference Laboratory, MLI 93-9, School of Information
Technology and Engineering, George Mason University, Fairfax, VA,
September 1993.
P93-26
Kaufman, K.A. and Michalski, R.S., "EMERALD: An
Integrated System of Machine Learning and Discovery Programs to
Support Education and Experimental Research," Reports
of the Machine Learning and Inference Laboratory, MLI 93-10,
School of Information Technology and Engineering, George Mason
University, Fairfax, VA, September 1993.
P93-27
Bala, J., Michalski, R.S. and Wnek, J., "The PRAX Approach
to Learning a Large Number of Texture Concepts," Technical
Report FS-93-04 Machine Learning in Computer Vision: What, Why
and How? AAAI Fall Symposium on Machine Learning in Computer
Vision, AAAI Press, Menlo Park, CA, October 1993.
P93-28
Bala, J. and Pachowicz, P.W., "Issues in Learning from Noisy
Sensory Data," Technical Report FS-93-04 Machine Learning in
Computer Vision: What, Why and How? AAAI Fall Symposium on
Machine Learning in Computer Vision, AAAI Press, Menlo Park,
CA, October 1993.
P93-29
Pachowicz, P.W., "Integration of Machine Learning and Vision
into an Active Agent Paradigm on the Example of Face Recognition
Problem," Technical Report FS-93-04, Machine Learning in
Computer Vision: What, Why and How? AAAI Fall Symposium on
Machine Learning in Computer Vision, AAAI Press, Menlo Park,
CA, October 1993.
P93-30
Imam, I.F. and Michalski, R.S., "Learning Decision Trees
from Decision Rules: A Method and Initial Results from a
Comparative Study," Journal of Intelligent Information
Systems JIIS, Vol. 2, No. 3, pp. 279-304, 1993.
P93-31
Vafaie, H. and De Jong, K.A., "Robust Feature Selection
Algorithms," Proceedings of the 5th International
Conference on Tools with Artificial Intelligence, Boston, MA,
November 1993.
P93-32
Michalski, R.S. and Wnek, J., "Constructive Induction: An
Automated Design of Knowledge Representation Spaces for Machine
Learning," Reports of the Machine Learning and Inference
Laboratory, MLI 93-11, School of Information Technology and
Engineering, George Mason University, Fairfax, VA, November 1993.
P93-33
Bloedorn, E., Wnek, J., Michalski, R.S. and Kaufman, K., "AQ17:
A Multistrategy Learning System: The Method and User's Guide,"
Reports of the Machine Learning and Inference Laboratory,
MLI 93-12, School of Information Technology and Engineering,
George Mason University, Fairfax, VA, November 1993.
P93-34
Guillen, L.E. Jr. and Wnek, J., "Investigation of Hypothesis-driven
Constructive Induction in AQ17," Reports of the Machine
Learning and Inference Laboratory, MLI 93-13, School of
Information Technology and Engineering, George Mason University,
Fairfax, VA, December 1993.
P93-35
Hieb, M.R. and Michalski, R.S., "Multitype Inference in
Multistrategy Task-adaptive Learning: Dynamic Interlaced
Hierarchies," Informatica: An International Journal of
Computing and Informatics, Vol. 17, No. 4, pp. 399-412,
December 1993.
P93-36
Michalski, R.S. and Tecuci, G., "Multistrategy Learning,"
in Kent, A. and Williams, J.G. (eds.), Encyclopedia of
Microcomputers, Vol. 12, Marcel Dekker, New York, 1993.
P93-37
Michalski, R.S. and Wnek, J., "Constructive Induction: An
Automated Improvement of Knowledge Representation Spaces for
Machine Learning," Proceedings of the 2nd Conference on
Practical Aspects of Artificial Intelligence, Augustow, IPI
PAN, Warszawa, Poland, pp. 188-236, 1993.
P93-38
Khasnabis, S., Arciszewski, T., Hoda, S.K. and Ziarko, W., "Automated
Knowledge Acquisition for Control of an Urban Rail Corridor,"
Proceedings of the Third International Conference on the
Applications of Artificial Intelligence to Civil and Structural
Engineering, Edinburgh, Scotland, 1993.
P93-39
Arciszewski, T. and Usmen, M., "Applications of Machine
Learning to Construction Safety," Proceedings of the
International Conference on Management of Information Technology
for Construction, Singapore, 1993.
P93-40
Arciszewski, T., Ziarko, W. and Khan, T.L., "Learning
Conceptual Design Rules: A Rough Sets Approach," Proceedings
of the International Workshop on Rough Sets, Banff, Alberta,
Canada, 1993.
P93-41
Arciszewski, T., "Learning Engineering: An Outline," Proceedings
of the ASCE Conference on Computing in Civil Engineering,
Anaheim, California, 1993.
P93-42
Seligman, L. and Kerschberg, L., "An Active Database
Approach to Consistency Management in Heterogeneous Data-and
Knowledge-based Systems," International Journal of
Cooperative and Intelligent Systems, Vol. 2, No. 2, October
1993.
P93-43
Seligman, L. and Kerschberg, L., "Federated Knowledge and
Database Systems: A New Architecture for Integrating of AI and
Database Systems," in Delcambre, L. and Petry, F. (eds.), Advances
in Databases and Artificial Intelligence, Vol. 1: The
Landscape of Intelligence in Database and Information Systems,
JAI Press, 1993.
P93-44
Yoon, J.P. and Kerschberg, L., "A Framework for Knowledge
Discovery and Evolution in Databases," IEEE Transactions
on Knowledge and Data Engineering, Vol. 5, No. 6, December
1993.
P93-45
Michalski, R.S., Carbonell, J., Mitchell, T. and Kodratoff, Y. (Eds.),
Apprentissage Symbolique: Une Approche de l'Intelligence
Artificielle, Tome I-II, French compilation of Machine
Learning: An Artificial Intelligence Approach, Vol. I-III,
Cepadues-Editions, 1993.
P93-46
Michalski, R.S. (Ed.), Multistrategy Learning, Kluwer
Academic Publishers, 1993.
P93-47
Michaels, G.S., Taylor, R., Hagstrom, R., Price, M. and Overback,
R., "Searching for Genomic Organizational Motifs:
Explorations of the E. coli Chromosome," Computers in
Chemistry, Vol. 17, pp.209-217, 1993.
P93-48
Michaels, G.S., Taylor, R., Hagstrom, R., Price, M. and Overback,
R., "Comparative Analysis of Genomic Data: A Global Look and
Structural and Regulatory Features," Proceedings of the
Second International Conference of Bioinformatics, Supercomputing
and Complex Genome Analysis, Hua A. Lim (Ed.), World
Scientific Publishing Co., River Edge, NJ, pp. 297-308, 1993.
P94-1
Michalski R.S. and Tecuci G. (Eds.), Machine Learning: A
Multistrategy Approach, Vol. IV, Morgan Kaufmann, San Mateo,
CA., 1994.
P94-2
Bala J. W., De Jong K.A. and Pachowicz P., "Multistrategy
Learning from Engineering Data by Integrating Inductive
Generalization and Genetic Algorithms," in Michalski, R.S.
and . Tecuci, G. (eds.), Machine Learning: A Multistrategy
Approach, Vol. IV, Morgan Kaufmann, San Mateo, CA, 1994.
P94-3
De Garis, H., "Genetic Programming: Evolutionary Approaches
to Multistrategy Learning," in Michalski, R.S. and . Tecuci,
G. (eds.), Machine Learning: A Multistrategy Approach, Vol. IV,
Morgan Kaufmann, San Mateo, CA, 1994.
P94-4
Michalski, R.S., "Inferential
Theory of Learning: Developing Foundations for Multistrategy
Learning," in Michalski, R.S. and . Tecuci, G. (eds.), Machine
Learning: A Multistrategy Approach, Vol. IV, Morgan Kaufmann,
San Mateo, CA, 1994.
P94-5
Vafaie, H. and De Jong, K.A., "Improving the Performance of
a Rule Induction System Using Genetic Algorithms," in
Michalski, R.S. and . Tecuci, G. (eds.), Machine Learning: A
Multistrategy Approach, Vol. IV, Morgan Kaufmann, San Mateo,
CA, 1994.
P94-6
Wnek, J. and Michalski, R.S., "Comparing Symbolic and
Subsymbolic Learning: Three Studies," in Michalski, R.S. and
. Tecuci, G. (eds.), Machine Learning: A Multistrategy
Approach, Vol. IV, Morgan Kaufmann, San Mateo, CA, 1994.
P94-7
Wnek, J. and Hieb, M, "Bibliography of Multistrategy
Learning Research," in Michalski, R.S. and Tecuci, G. (eds.),
Machine Learning: A Multistrategy Approach, Vol. IV,
Morgan Kaufmann, San Mateo, CA, 1994.
P94-8
Zhang, J., "Learning Graded Concept Descriptions by
Integrating Symbolic and Subsymbolic Approaches," in
Michalski, R.S. and . Tecuci, G. (eds.), Machine Learning: A
Multistrategy Approach, Vol. IV, Morgan Kaufmann, San Mateo,
CA, 1994.
P94-9
Wnek, J. and Michalski, R.S., "Hypothesis-driven
Constructive Induction in AQ17-HCI: A Method and Experiments,"
Machine Learning, Vol. 14, No. 2, pp. 139-168, 1994.
P94-10
Vafaie, H. and Imam, I.F., "Feature Selection Methods:
Genetic Algorithm vs. Greedy-like Search," Proceedings of
the 3rd International Fuzzy Systems and Intelligent Control
Conference, Louisville, KY, March 1994.
P94-11
Wnek, J. and Michalski, R.S., "Symbolic Learning of M-of-N
Concepts," Reports of the Machine Learning and Inference
Laboratory, MLI 94-1, School of Information Technology and
Engineering, George Mason University, Fairfax, VA, April 1994.
P94-12
Bloedorn, E., Michalski, R.S. and Wnek, J., "Matching
Methods with Problems: A Comparative Analysis of Constructive
Induction Approaches," Reports of the Machine
Learning and Inference Laboratory, MLI 94-2, School of
Information Technology and Engineering, George Mason University,
Fairfax, VA, May 1994.
P94-13
Imam, I.F. and Vafaie, H., "An Empirical Comparison Between
Global and Greedy-like Search for Feature Selection," Proceedings
of the 7th Florida Artificial Intelligence Research Symposium (FLAIRS-94),
pp. 66-70, Pensacola Beach, FL, May 1994.
P94-14
Tischer, L. and Bloedorn, E., "An Application of Machine
Learning to GIS Analysis," Proceedings of the ESRI-94
User Conference, CA, May 1994.
P94-15
Imam, I.F., "An Experimental Study of Discovery in Large
Temporal Databases," Proceedings of the Seventh
International Conference on Industrial and Engineering
Applications of Artificial Intelligence and Expert Systems (IEA/AIE-94),
Austin, TX, pp. 171-180, June 1994.
P94-16
Arciszewski, T., Bloedorn, E., Michalski, R.S., Mustafa, M. and
Wnek, J., "Machine
Learning of Design Rules: Methodology and Case Study," ASCE
Journal of Computing in Civil Engineering, Vol. 8, No. 3, pp.
286-308, July 1994.
P94-17
Sazonov, V.N. and Wnek, J., "Hypothesis-driven Constructive
Induction Approach to Expanding Neural Networks," Working
Notes of the ML-COLT'94 Workshop on Constructive Induction and
Change of Representation, New Brunswick, NJ, July 1994.
P94-18
Wnek, J. and Michalski, R.S., "Discovering Representation
Space Transformations for Learning Concept Descriptions Combining
DNF and M-of-N Rules," Working Notes of the ML-COLT'94
Workshop on Constructive Induction and Change of Representation,
New Brunswick, NJ, July 1994.
P94-19
Arciszewski, T., Khasnabis, S., Hoda, S.K. and Ziarko, W, "Machine
Learning in Transportation Engineering: A Feasibility Study,"
Journal of Applied Artificial Intelligence, Vol. 8, No. 1,
1994.
P94-20
Arciszewski, T., Borkowski, A., Dybala, T., Racz, J. and Wojan, P.,
"Empirical Comparison for Symbolic and Subsymbolic Learning
Systems," Proceedings of the First International ASCE
Congress on Computing in Civil Engineering, Washington, D.C.,
1994.
P94-21
Arciszewski, T., "Machine Learning in Engineering Design,"
Proceedings of the Conference on Intelligent Information
Systems, Institute of Computer Science, Polish Academy of
Sciences, Wigry, Poland, 1994.
P94-22
Arciszewski, T. and Michalski, R.S., "Inferential Design
Theory: A Conceptual Outline," Proceedings of the Third
International Conference on Artificial Intelligence in Design,
Lausanne, Switzerland, 1994.
P94-23
Imam, I.F. and Michalski, R.S., "From Fact to Rules to
Decisions: An Overview of the FRD-1 System," Proceedings
of the AAAI-94 Workshop on Knowledge Discovery in Databases,
Seattle, WA, pp. 229-236, August, 1994.
P94-24
Kaufman, K., "Comparing International
Development Patterns Using Multi-operator Learning and Discovery
Tools," Proceedings of the AAAI-94 Workshop on Knowledge
Discovery in Databases, Seattle, WA, pp. 431-440, August, 1994.
P94-25
Maloof, M. and Michalski, R.S., "Learning
Descriptions of 2D Shapes for Object Recognition and X-Ray Images,"
Reports of the Machine Learning and Inference Laboratory, MLI
94-4, George Mason University, Fairfax, VA, October 1994.
P94-26
Michalski, R.S. and Ram, A., "Learning as Goal-Driven
Inference," Reports of the Machine Learning and Inference
Laboratory, MLI 94-5, George Mason University, Fairfax, VA,
October 1994.
P94-27
Michalski, R.S., Rosenfeld, A., and Aloimonos, Y., "Machine
Vision and Learning: Research Issues and Directions," Reports
of the Machine Learning and Inference Laboratory, MLI 94-6,
George Mason University, Fairfax, VA; Reports of the Center
for Automation Research CAR-TR-739, CS-TR-3358, University of
Maryland, College Park, MD, October 1994.
P94-28
Michalski, R.S. and Imam, I.F., "Learning Problem-Oriented
Decision Structures from Decision Rules: The AQDT-2 System,"
in Lecture Notes in Artificial Intelligence, Methodology for
Intelligent Systems of the 8th International Symposium on
Methodology for Intelligent Systems (ISMIS-94), Z.W. Ras
& M. Zemankova (Eds.) No. 869, pp. 416-426, October, 1994.
P94-29
Michaels, G.S., "Bioinformatics or Biology?" Chemical
Design Automation News, Vol. 8, pp. 1-34, 1994.
P94-30
Zull, J.E., Taylor, R.C., Michaels, G.S., and Rushforth, N.,
"Nucleic Acid Sequences Coding for Internal Antisence
Peptides: Are There Implications for Protein Folding and
Evolution?" Nucleic Acid Research, 1994.
P94-31
Wnek, J. and Michalski, R.S., "Conceptual
Transition from Logic to Arithmetic," Reports of the
Machine Learning and Inference Laboratory, MLI 94-7, George
Mason University, Fairfax, VA, December 1994.
P94-32
Michalski, R.S. "Seeking Knowledge in the Flood of Facts,"
Proceedings of the Conference on Intelligent Information
Systems, Institute of Computer Science, Polish Academy of
Sciences, Wigry, Poland, 1994.
P94-33
Pachowicz, P.W. and Bala, J.W., "A Noise-Tolerant Approach
to Symbolic Learning from Sensory Data," Journal of
Intelligent and Fuzzy Systems, Vol. 2, pp. 347-361, John
Wiley & Sons, Inc., 1994.
P94-34
Bala, J.W., Pachowicz, P.W. and Michalski, R.S., "Progress
on Vision Through Learning at George Mason University," Proceedings
of the ARPA Image Understanding Workshop, November 13-16,
1994.
P95-1
Michalski R.S. and Wnek, J. (Eds.), "Center for Machine
Learning and Inference: An Overview of Research and Activities,"
Reports of the Machine Learning and Inference Laboratory,
MLI 95-1, George Mason University, Fairfax, VA, January 1995.
P95-2
Maloof, M. and Michalski R.S, "A
Partial Memory Incremental Learning Methodology and its
Application to Computer Intrusion Detection," Reports
of the Machine Learning and Inference Laboratory, MLI 95-2,
George Mason University, Fairfax, VA, March 1995.
Abstract. This paper discusses work in progress and introduces a partial memory incremental learning methodology. The incremental learning architecture uses hypotheses induced from training examples to determine representative examples, which are maintained for future learning. Criticism and reinforcement from the environment or the user invoke incremental learning once the system is deployed. Such an architecture and development methodology is necessary for applications involving intelligent agents, active vision, and dynamic knowledge-bases. For this study, the methodology is applied to the problem of computer intrusion detection. Several experimental comparisons are made using batch and incremental learning between AQ15c, a feed-forward neural network, and k-nn. Experimental results suggest that AQ15c has several advantages over other methods in terms of predictive accuracy, incremental learning, learning and recognition times, the types of concepts induced by the method, and the types of data from which these methods can learn.
P95-3
Bloedorn, E., Imam, I., Kaufman, K., Maloof, M., Michalski, R.S.
and Wnek, J., "HOW DID AQ FACE
THE EAST-WEST CHALLENGE? An Analysis of the AQ Family's
Performance in the 2nd International Competition of Machine
Learning Programs," Reports of the Machine Learning
and Inference Laboratory, MLI 95-3, George Mason University,
Fairfax, VA, March 1995.
Abstract. The "East-West Challenge" is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and Ashwin Srinivasan from Oxford University. The goal of the competition was to solve the "TRAINS problems," that is to discover the "simplest" classification rules for train-like structured objects. The rule complexity was judged by a Prolog program that counted the number of various components in the rule expressed in the from of Prolog Horn clauses. There were 65 entries from several countries submitted to the competition. The GMU team's entry was generated by three members of the AQ family of learning programs: AQ-DT, INDUCE and AQ17-HCI. The paper analyses the results obtained by these programs and compares them to those obtained by other learning programs. It also presents ideas for further research that were inspired by the competition. One of these ideas is a challenge to the machine learning community to develop a measure of knowledge complexity that would adequately capture the "cognitive complexity" of knowledge. A preliminary measure of such cognitive complexity, called C-complexity, different from the Prolog-complexity (P-complexity) used in the competition, is briefly discussed.
P95-4
Wnek, J., Kaufman, K., Bloedorn, E. and Michalski, R.S., "Inductive
Learning System AQ15c: The Method and User's Guide," Reports
of the Machine Learning and Inference Laboratory, MLI 95-4,
George Mason University, Fairfax, VA, March 1995.
Abstract. AQ15c is a system for acquiring decision or classification rules from examples and counterexamples and/or from previously learned decision rules. When learning rules, AQ15c uses 1) background knowledge in the form of rules, 2) the definition of descriptors and their types and 3) a rule preference criterion that evaluates competing candidate hypotheses. Each training example characterizes an object, and its class-label specifies the correct decision associated with that object. The generated decision rules are expressed as symbolic descriptions involving relations between objects' attribute values. Rule generation is guided by a user-defined rule-preference criterion. The user-defined criterion ranks the importance and tolerance of a number of measures of rule quality including rule complexity, cost and coverage. AQ15c is a C language re-implementation of AQ15 (Hong, Mozetic, Michalski, 1986) written in Pascal. This version can handle larger datasets, is more robust and portable. Versions of AQ15c have so far been compiled for the Sun Solaris, IBM-compatible, and Apple platforms. In addition the testing facilities have been expanded to include three different measures of match.
P95-5
Wnek, J., "DIAV 2.0 User Manual:
Specification and Guide through the Diagrammatic Visualization
System," Reports of the Machine Learning and
Inference Laboratory, MLI 95-5, George Mason University,
Fairfax, VA, 1995.
Abstract. The goal of the diagrammatic visualization system DIAV is to provide a tool for a visual interpretation of various aspects of concept learning. These include: visualization of knowledge representation spaces and relationships between training examples and target and learned concepts, and visual comparison of knowledge transmutations performed by various learning systems, e.g. visualization of changes in the representation space done by constructive induction. The system employs a planar model of a multidimensional space spanned over a set of discrete attributes. The model is in the form of a diagram, in which each cell represents a unique combination of attribute values. The diagram can represent examples, rules, and rulesets (DNF) in the form of concept images. The system is very useful for analyzing behavior of existing learning algorithms and in every stage of development of a new learning system.
P95-6
Arciszewski, T., Michalski, R. and Dybala, T., "STAR
Methodology-Based Learning about Construction Accidents and their
Prevention," Journal of Construction Automation, Vol.
4, pp. 75-85, 1995.
Abstract. This paper presents the results of a feasibility study concerning the application of STAR methodology-based machine learning to construction accidents and their prevention. A ten-stage knowledge acquisition process is presented and its individual stages described. Knowledge about construction accidents was acquired using a collection of 225 examples, based on actual accidents records. Inductive learning with a system based on the STAR methodology was employed. This system was used in both the generalization and specialization modes of operation. The decision rules obtained are complex, but their interpretation is clear and they seem to be consistent with the present understanding of causal relationships between accident results and various factors affecting them. Also, the rules were verified using average overall and omission empirical error rates, which were calculated as average for three randomly determined sequences of examples. These error rates were calculated for all seven steps in the machine learning process, and were used to construct learning curves for both error rates. The relationships between error rates and the number of examples used for learning are analyzed, and coefficients of linear regression given and discussed. The 225 examples used were found to be grossly insufficient to produce reliable knowledge about accidents and therefore a large study is postulated which would involve the collection of a larger number of construction accident records. In general, our study demonstrated the feasibility of machine learning in acquiring knowledge about construction accidents.
P95-7
Imam, I. and Wnek, J. (Eds.), Proceedings of the First
International Workshop on Intelligent Adaptive Systems (IAS-95),
Melbourne Beach, FL, April 26, 1995.
P95-8
Bloedorn, E. and Wnek, J., "Constructive
Induction-based Learning Agents: An Architecture and Preliminary
Experiments," Proceedings of the First International
Workshop on Intelligent Adaptive Systems (IAS-95), Melbourne
Beach, FL, pp. 38-49, April 26, 1995.
Abstract. This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agent's ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CI- based learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an architecture for a CI-based learning agent and gi ves an empirical comparison of a CI and SI for a set of six abstract domains involving DNF-type (disjunctive normal form) descriptions.
P95-9
Imam, I., "Intelligent Agents for Management of Learning: An
Introduction and a Case Study," Proceedings of the First
International Workshop on Intelligent Adaptive Systems (IAS-95),
Melbourne Beach, FL, pp. 95-106, April 26, 1995.
Abstract. Machine learning systems are widely used for obtaining useful knowledge that can assist the users in different ways. Most learning systems are equipped with a set of learning parameters to adapt the learning algorithm for different kinds of problems. Since many users exert the least effort possible, these parameters may not be set properly, or they may be used with their default values. This may cause problems resulting in learning less accurate or more complex knowledge. This paper proposes an initial framework for building intelligent agents that assist the users in managing the learning process using the available set of learning parameters. These agents gain initial knowledge through experimental runs. Then they use this initial knowledge to set the learning parameters whenever the learning system is used later. The agents adapt their knowledge, using an explanation-based learning approach, when a new contradictory feedback from the user is given. A case study is introduced for building intelligent agents for managing the learning system AQ15. The results were tested on two of the MONKs problems.
P95-10
Arciszewski, T., Michalski, R.S. and Wnek, J., "Constructive Induction: The
Key to Design Creativity," Reports of the Machine
Learning and Inference Laboratory, MLI 95-6, Machine Learning
and Inference Laboratory, George Mason University, Fairfax, VA,
April 1995.
Abstract. The paper presents initial results from an emerging new direction in engineering design research, in particular, creative design. It argues that constructive induction, which was originally proposed in the field of machine learning, can serve as a foundation for developing a computational theory of engineering design and design creativity. Constructive induction is a process of creating new knowledge (e.g., design knowledge) by performing two intertwined searches, one-for the most adequate knowledge representation space, and second-for the best hypothesis in this space. Basic concepts and methods of constructive induction are reviewed and illustrated by examples of their application to conceptual structural design. Several crucial design concepts, including those of an emergent concept and of a goal-oriented transformation of the design representation space are interpreted in terms of a construction induction process. It is also shown how constructive induction applies to the control of the design creativity level. Several measures of the design complexity and relative creativity are proposed. The conclusion presents some unresolved problems and a plan for future research.
P95-11
Chen, Q. and Arciszewski, T., "Machine Learning of Bridge
Design Rules: A Case Study," Proceedings of the 2nd ASCE
Congress on Computing in Civil Engineering, Atlanta GA, June,
1995.
Abstract. The paper describes an experimental application of a machine learning system to the automated knowledge acquisition in the area of preliminary design of cable-stayed bridges. Design examples were produced using ICADC, a knowledge based system for the preliminary design of cable-stayed bridges which was developed for the practical design purpose at Dalian University of Technology in the People's Republic of China. Learning experiments were conducted using INLEN, a learning system based on the AQ15 learning algorithm, both developed at George Mason University. The entire process of knowledge acquisition is described, including the preparation of examples, learning process, and knowledge verification which was conducted using various empirical error rates. Also, the final conclusions and an outline of future research are provided.
P95-12
Ribeiro, J., Kaufman, K. and Kerschberg, L., "Knowledge
Discovery from Multiple Databases," Proceedings of the
IASTED/ISMM International Conference on Intelligent Information
Management Systems, Washington, D.C., June, 1995.
Abstract. Knowledge discovery systems for databases are employed to provide valuable insights into characteristics and relationships that may exist in the data, but are unknown to the user. This paper describes a methodology and system for performing knowledge discovery across multipl databases. These enhancements have been integrated into the prototype knowledge discovery system called INLEN. The enhancements include the incorporation of primary and foreign keys as well as the development and processing of knowledge segments.
P95-13
Michalski, R.S. and Wnek, J., "Learning Hybrid Descriptions,"
Proceedings of the 4th International Symposium on Intelligent
Information Systems, Augustow, Poland, June 5-9, 1995.
Abstract. Most symbolic learning methods are concerned with learning concept descriptions in the form of a decision tree or a set of rules expressed in terms of the originally given attributes. For some practical problems, these methods are inadequate because they cannot learn conditions that require counting of some object properties. Such problems occur, for example, in engineering, economy, medicine and software engineering. This paper describes a method for learning hybrid descriptions that combine logic-type and arithmetic-type properties. The presented method builds hybrid descriptions in the form of conditional counting rules, which are logic-type (DNF) expressions with counting conditions (expressing a relationship involving a count of some object properties). The method employs a constructive induction approach in which the learning system performs two intertwined searches: one-for the most appropriate knowledge representation space, and second-for the "best" hypothesis in the space. The first search is done by determining maximum symmetry classes of binary attributes in the initial DNF-type hypotheses, and extending the initial representation space by counting attributes that correspond to these symmetry classes. The search for the "best" hypothesis in so extended representation space is done by a standard AQ inductive rule learning program. It our experiments, the proposed method learned simple and accurate concept descriptions when conventional learning methods failed.
P95-14
Michalski, R.S., "Learning and Cognition,"
Invited talk at 2nd International World Conference on the Foundations of
Artificial Intelligence, Paris, July 3-7, 1995.
Abstract. This paper presents a general definition of
intelligence in terms of the basic capabilites that are required
regardless of whether it is exhibited in natural or designed
systems. The degree to which a system possesses these
capabilities determines the degree of intelligence. The paper
then discusses three interrelated topics:
1) Relationship among basic concepts underlying intelligent
behavior such as learning, cognition and inference
2) Capabilities of brains vs. computers
3) Inferential Theory of Learning that provides a unifying
framework for learning processes.
P95-15
Szczepanik, W., Arciszewski, T. and Wnek, J., "Empirical
Performance Comparison of Two Symbolic Learning Systems Based On
Selective And Constructive Induction," Proceedings of the
IJCAI-95 Workshop on Machine Learning in Engineering,
Montreal, Canada, August, 1995.
Abstract. The paper provides results of a performance comparison study of two symbolic learning programs, both based on the AQ15c learning algorithm. The first program uses the single representation space while the second utilizes constructive induction learning, which incorporates changes in the representation space. The performance of the compared systems was analyzed using the overall empirical error rates determined using the leaning-one-out and hold-out sampling methods. Both system's performance was calculated for individual stages in a multi-stage knowledge acquisition process, and learning curves and their envelopes were prepared. The study was conducted using the set of 384 optimal designs of wind bracing in steel skeleton structures of tall buildings.
P95-16
Ribeiro, J., Kaufman, K. and Kerschberg, L.,
"Knowledge
Discovery from Multiple Databases," Proceedings of the
First International Conference on Knowledge Discovery and Data
Mining (KDD-95), Montreal, Canada, pp. 240-245, August, 1995.
Abstract. Knowledge discovery systems for databases are employed to provide valuable insights into characteristics and relationships that may exist in the data, but are unknown to the user. This paper describes a methodology and system for performing knowledge discovery across multipl databases. These enhancements have been integrated into the prototype knowledge discovery system called INLEN. The enhancements include the incorporation of primary and foreign keys as well as the development and processing of knowledge segments.
P95-17
Maloof, M.A. and Michalski, R.S., "Learning Symbolic Descriptions of
2D Shapes for Object Recognition in X-ray Images," Proceedings
of the 8th International Symposium on Artificial Intelligence,
Monterrey, Mexico, October 17-20, 1995.
Abstract. This paper describes a method for learning shape descriptions of 2D objects in x-ray images. The descriptions are induced from shape examples using the AQ15c inductive learning system. The method has been experimentally compared to k-nearest neighbor, a statistical pattern recognition technique, and artificial neural networks. Experimental results demonstrate strong advantages of the AQ methodology over the other methods. Specifically, the method has higher predictive accuracy and faster learning and recognition rates. The application considered is detecting blasting caps in x-ray images of luggage. An intelligent system performing this detection task can be used to assist airport security personnel with luggage screening.
P95-18
Maloof, M.A. and Michalski, R.S., "A Partial Memory Incremental
Learning Methodology and its Application to Intrusion Detection,"
Proceedings of the 7th IEEE International Conference on Tools
with Artificial Intelligence, Herndon, VA, 1995.
Abstract. This paper describes a partial-memory incremental learning method based on the AQ15c inductive learning system. The method maintains a representative set of past training examples that are used together with new examples to appropriately modify the currently held hypotheses. Incremental learning is evoked by feedback from the environment or from the user. Such a method is useful in applications involving intelligent agents acting in a changing environment, active vision, and dynamic knowledge-bases. For this study, the method is applied to the problem of computer intrusion detection in which symbolic profiles are learned for a computer system users. In the experiments, the proposed method yielded significant gains in terms of learning time and memory requirements at the expense of slightly lower predictive accuracy and higher concept complexity, when compared to batch learning, in which all examples are given at once.
P95-19
Vafaie, H. and De Jong, K.A., "Genetic
Algorithm as a Tool for Restructuring Feature Space
Representations," Proceedings of the 7th IEEE
International Conference on Tools with Artificial Intelligence,
Herndon, VA, 1995.
Abstract. This paper describes an approach being explored to improve the usefulness of machine learning techniques to classify complex, real world data. The approach involves the use of genetic algorithms as a "front end" to a traditional tree induction system (ID3) in order to find the best feature set to be used by the induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate significant advantages of the presented approach.
P95-20
Imam, I.F., "Deriving Task-oriented Decision Structures From
Decision Rules," Ph.D. dissertation, School of
Information Technology and Engineering, Reports of the Machine
Learning and Inference Laboratory, MLI 95-7, George Mason
University, Fairfax, VA, October 1995.
Abstract. This dissertation is concerned with research
on learning task-oriented decision structures from decision rules.
The philosophy behind this research is that it is more
appropriate to learnknowledge and store it in a declarative form,
and then when a decision making situation occurs, generate from
this knowledge the decision structure that is most suitable for
the given decision making situation. Learning decision structures
from decision rules was first introduced by Michalski(1978). The
first implementation of this approach was done by Imam and
Michalski(1993), called AQDT. This approach separates the
function of generating a knowledge-base from the function of
using the knowledge-base for decision-making. The first function
focuses on learning accurate,consistent and complete concept
description expressed in a declarative form. The second function
is performed whenever a new decision-making situation occurrs; a
task-oriented decision structureis obtained to suit that
situation. Task-oriented knowledge is defined as knowledge that
is adapted for solving a given decision-making situation (Imam
& Michalski, 1994; Michalski & Imam,1994).
The dissertation introduces the system AQDT-2 for learning task-oriented
decision structures from decision rules or examples. Each
decision making situation is defined by a set of parameters that
controls the learning process of the AQDT-2 system. The extensive
experiments on AQDT-2 show that decision structures learned by it
usually outperform, in terms of accuracy and average size of the
decision structures, those learned from examples by other well
known systems. The results show also that the system does not
work very well with noisy data. The system is illustrated and
compared using applications of artificial problems such as the
three MONK's problems (Thrun, Mitchell & Cheng, 1991) and the
East-West Train problem (Michie, et al, 1994). It was also
applied to real world problems of learning decision structures in
the areas of construction engineering (for determining the best
wind bracing design for tall buildings), medical diagnosis (for
learning decision rules for recognizing breast cancer),
agriculture diagnosis (for learning classification rules for
distinguishing between poisonous and non-poisonous mushrooms),
and political data (for characterizing democratic and republican
voting records).
P95-21
Michalski, R.S. and Ram A., "Learning
as Goal-Driven Inference," in Goal-Driven Learning,
A. Ram & D. B. Leake (Eds.), MIT Press/Bradford Books,
Cambridge, MA, 1995.
Abstract. In this chapter, we will focus on the inferential theory of learning and its role in goal-driven learning. In particular, we will view learning as a guided or planful search through a knowledge space--the space of knowledge representations that the learner can represent or potentially generate. This search is actively guided by the learning goals of the system. The operators of the search are instantiations of generic types of knowledge transmutations, each capable of changing knowledge in some fundamental manner. Learning, then, is the goal-directed transformation of knowledge; this transformation is carried out through the basic inferential processes that are embodied in knowledge transmutation operators. The early ideas underlying the development of the inferential theory of learning go back to Michalski(1983). A classification and illustration of different learning strategies based on the criteria developed in the theory can be found in Michalski(1993).
P95-22
Maloof, M.A. and Michalski, R.S., "Learning Evolving Concepts Using a
Partial Memory Approach," Proceedings of the AAAI
1995 Fall Symposium on Active Learning, Cambridge, MA,
November 10-12, 1995.
Abstract. This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example, building intelligent agents for helping users in Internet search, active vision, automatically updating knowledge-bases, or acquiring profiles of users of telecommunication networks. Requirements for a learning architecture supporting such applications include the ability to incrementally modify concept definitions to accommodate new information, fast learning and recognition rates, low memory needs, and the understandability of computer-created concept descriptions. To address these requirements, we propose a learning architecture based on Variable-Valued Logic, the Star Methodology, and the AQ algorithm. The method uses a partial- memory approach, which means that in each step of learning, the system remembers the current concept descriptions and specially selected representative examples from the past experience. The developed method has been experimentally applied to the problem of computer system intrusion detection. The results show significant advantages of the method in learning speed and memory requirements with only slight decreases in predictive accuracy and concept simplicity when compared to traditional batch-style learning in which all training examples are provided at once.
P95-23
Arciszewski, T., Michalski, R.S., Wnek, J., "Constructive Induction: the
Key to Design Creativity," Proceedings of the Third
International Round-Table Conference on Computational Models of
Creative Design, Heron Island, Queensland, Australia, pp. 397-425,
December 3-7, 1995.
Abstract. The paper presents initial results from an emerging new direction in engineering design research, in particular, creative design. It argues that constructive induction, which was originally proposed in the field of machine learning, can serve as a foundation for developing a computational theory of engineering design and design creativity. Constructive induction is a process of creating new knowledge (e.g., design knowledge) by performing two intertwined searches, one-for the most adequate knowledge representation space, and second-for the best hypothesis in this space. Basic concepts and methods of constructive induction are reviewed and illustrated by examples of their application to conceptual structural design. Several crucial design concepts, including those of an emergent concept and of a goal-oriented transformation of the design representation space are interpreted in terms of a construction induction process. It is also shown how constructive induction applies to the control of the design creativity level. Several measures of the design complexity and relative creativity are proposed. The conclusion presents some unresolved problems and a plan for future research.
P95-24
Zhang, J. and Michalski, R.S., "An Integration of Rule
Induction and Exemplar- Based Learning for Graded Concepts,"
Machine Learning, Vol.21, No.3, Kluwer Academic Publishers,
pp. 235-268, December 1995.
Abstract. This paper presents a method for learning graded concepts. Our method uses a hybrid concept representation that integrates numeric weights and thresholds with rules and combines rules with exemplars. Concepts are learned by constructing general descriptions to represent common cases. These general descriptions are in the form of decision rules with weights on conditions, interpreted by a similarity measure and numeric thresholds. The exceptional cases are represented as exemplars. This method was implemented in the Flexible Concept Learning System (FCLS) and tested on a variety of problems. The testing problems included practical concepts, concepts with graded structures, and concepts that can be defined in the classic view. For comparison, a decision tree learning system, an instance-based learning system, and the basic rule variant of FCLS were tested on the same problems. The results have shown a statistically meaningful advantage of the proposed method over others both in terms of classification accuracy and description simplicity on several problems.
P95-25
Michalski, R.S. and Wnek, J., "Presentation Notes of the
Annual Review of Research in Machine Learning and Inference,"
Machine Learning and Inference Laboratory, George Mason
University, Fairfax, VA, May 19, 1995.
P95-26
Arciszewski, T., Bloedorn, E. Michalski, R.S., Mustafa, M. and
Wnek, J., "Machine Learning in Engineering Design: A
Methodology and Case Study," Reports of the Machine
Learning and Inference Laboratory, MLI 95-8, George Mason
University, Fairfax, VA, December, 1995.
P96-1
Michalski R.S. and Wnek, J. (Eds.), "Machine Learning and
Inference Laboratory: An Overview of Research and Activities,"
Reports of the Machine Learning and Inference Laboratory,
MLI 96-1, George Mason University, Fairfax, VA, January 1996.
P96-2
Publication List of Machine Learning and Inference Laboratory
Part 1: 1969-1987, MLI 96-2, George Mason University, Fairfax,
VA, January 1996.
P96-3
Publication List of Machine Learning and Inference Laboratory
Part 2: 1988-1995, MLI 96-3, George Mason University, Fairfax,
VA, January 1996.
P96-4
Kaufman, K. and Michalski, R.S., "A Multistrategy
Conceptual Analysis of Economic Data," Ein-Dor, P. (ed.),
Artificial Intelligence in Economics and Management: An Edited
Proceedings on the Fourth International Workshop, Boston,
Kluwer Academic Publishers, pp. 193-203, 1996.
Abstract. The goal of the multistrategy tool, INLEN, is to serve as an intelligent assistant for discovering knowledge in large databases. INLEN has been applied to, and is well-suited for the exploration of databases consisting of economic and demographic facts and statistics. Preliminary experiments on several data sets have focused on discerning and comparing various patterns in the status and development of countries in different regions of the world. These experiments have provided some interesting and often unexpected results, and serve as an example of one way in which such data can be explored. This paper describes in brief the INLEN methodology, presents examples of its learning and discovery operators, and demonstrates its application to economic domains.
P96-5
Michalski R.S., Rosenfeld, A., Aloimonos Y., Duric, Z., Maloof M.A.
and Zhang Q., "Progress On Vision Through Learning: A
Collaborative Effort of George Mason University and University of
Maryland," Proceedings of the Image Understanding
Workshop, Palm Springs, CA, Feburary, 1996.
Abstract. This report briefly reviews research progress on vision through learning conducted as a collaborative effort of the GMU Machine Learning and Inference Laboratory and the UMD Computer Vision Laboratory. The report covers work done on the following projects: (1) The Multi-level Image Sampling and Transformation (MIST) methodology for learning image descriptions and transformations (2) Applying the MIST methodology to semantic analysis of outdoor scenes (3) Recognizing objects in a cluttered environment (4) Learning in navigation (5) Intelligent interfaces: Learning in the RADIUS environment (6) Learning space configuration and homing (7) Learning object functionality Our work aims at ultimately developing vision systems that apply a range of symbolic and parametric machine learning methods to solving vision problems.
P96-7
Michalski, R.S., Zhang, Q., Maloof, M.A. and Bloedorn. E., "The MIST
Methodology and its Application to Natural Scene Interpretation,"
Proceedings of the Image Understanding Workshop, Palm
Springs, CA, pp. 1473-1479, Feburary, 1996.
Abstract. The MIST methodology (Multi-level Image Sampling and Transformation) provides an environment for applying diverse machine learning methods to problems of computer vision. The methodology is illustrated by a problem of learning how to conceptually interpret natural scenes. In the experiments described, three learning programs were used: AQ15c-for learning decision rules from examples, NN-neural net learning, and AQ-NN-multistrategy learning combining symbolic and neural net methods. Presented results illustrate the performance of the learning programs for the chosen problem of natural scene interpretation in terms of predictive accuracy, training time, recognition time, and complexity of the induced descriptions. The MIST methodology has proven to be very useful for the presented application. Overall, the experiments performed indicate that the multistrategy learning program AQ-NN appears to be the most promising approach.
P96-8
Duric, Z., Rivlin, E. and Rosenfeld, A., "Learning an Object's
Function by Observing the Object in Action," Proceedings
of the Image Understanding Workshop, Palm Springs, CA,
February, 1996.
Abstract. One way to learn the function of an object is
by watching the object in use. As an axample an observer might
"see" a knife being used to slice bread and learn the
function of cutting and the context in which it can be used.
This paper demonstrates that the function of an object can be
inferred from its motion. We show that the motion of an object,
when combined with information about object's shape, provides
strong constraints on possible functions that the object might be
performing. In further studies, currently in progress, we will
demonstrate that this approach can be used to learn the
functionality of an unknown object by observing an image sequence
that shows the object performing an action which accomplishes the
function.
P96-9
Maloof, M.A., Duric, Z., Michalski, R.S. and Rosenfeld, A.,
"Recognizing Blasting Caps in X-Ray Images," Proceedings
of the Image Understanding Workshop, Palm Springs, CA,
Feburary, 1996.
Abstract. This paper presents work in progress on an approach to the problem of recognizing blasting caps in x-ray images. An analysis of functional properties of blasting caps was used to design the representation space, which combines intensity and shape features. Recognition proceeds in two phases. The first phase is a bottom-up process in which low intensity blobs are used as attention-catching devices to generate object hypotheses. The second phase is a top-down process in which object hypotheses are confirmed or rejected by fitting a local model to ribbons surrounding the low intensity blob. The local model is acquired using inductive learning. Flexible matching routines are used during recognition that provide a measure of confidence for the identification. Experimental results demonstrate the ability to learn the relationship between image characteristics and object functionality.
P96-10
Imam, I.F., "The AQDT-2 USER'S GUIDE: A Machine Learning
Program for Learning Task-oriented Decision Structures from
Decision Rules," Reports of the Machine Learning and Inference
Laboratory, MLI 96-4, George Mason University, Fairfax, VA, March
1996.
P96-11
Imam, I.F., "The AQDT-2 PROGRAMMER'S GUIDE: A Machine
Learning Program for Learning Task-oriented Decision Structures
from Decision Rules," Reports of the Machine Learning and
Inference Laboratory, MLI 96-5, George Mason University,
Fairfax, VA, March 1996.
P96-12
Michalski, R.S. and Wnek, J. (Eds.), Proceedings of the Third
International Workshop on Multistrategy Learning (MSL-96),
Harpers Ferry, WV, May 23-25, 1996.
P96-13
Alkharouf, N.W. and Michalski, R.S., "Multistrategy Task-Adaptive
Learning Using Dynamic Interlaced Hierarchies: A Methodology and
Initial Implementation of INTERLACE," Proceedings of the
Third International Workshop on Multistrategy Learning (MSL-96),
Harpers Ferry, WV, pp. 117-124, May 23-25, 1996.
Abstract. This research concerns the development of a methodology for representing, planning and executing multitype inferences in a multistrategy task-adaptive learning system. These inferences, defined in the Inferential Theory of Learning as knowledge transmutations, are generic types of knowledge operators, and are assumed to underlie all learning processes. The paper shows how several basic knowledge transmutations can be seamlessly integrated using a knowledge representation based on dynamic interlaced hierarchies (DIH). The implemented system, INTERLACE, includes an interactive graphical user interface for visualizing knowledge transmutations that are being performed by the system. INTERLACE is illustrated by several examples.
P96-14
Kaufman, K., "Addressing Knowledge Discovery
Problems in a Multistrategy Framework," Proceedings of the
Third International Workshop on Multistrategy Learning (MSL-96),
Harpers Ferry, WV, pp. 305-312, May 23-25, 1996.
Abstract. This paper discusses a methodology for multistrategy data analysis based on the application of diverse learning and discovery programs and tools and how it approaches some of the difficulties posed by the knowledge discovery task. Research in the area of integrated learning systems has led to the development of INLEN, an intelligent assistant for discovering knowledge in large databases. The architecture of INLEN is based on the interaction of a number of knowledge generation operators - manifestations of diverse learning tools within a uniform environment. Examples of the system's application to databases consisting of world economic and demographic facts demonstrate its operation. During its development, INLEN has encountered problems inherent in the application of symbolic learning programs to data analysis that do not appear in the laboratory environment; such problems are described, and the responses to these problems that have been built into INLEN are discussed.
P96-15
Bloedorn, E. and Michalski, R.S., "The
AQ17-DCI System for Data-Driven Constructive Induction and Its
Application to the Analysis of World Economics," Proceedings
of the Ninth International Symposium on Methodologies for
Intelligent Systems (ISMIS-96), Zakopane, Poland, June 10-13,
1996.
P96-16
Imam, I.F. and Michalski, R.S., "An Empirical Comparison
Between Learning Decision Trees from Examples and from Decision
Rules," Proceedings of the Ninth International Symposium
on Methodologies for Intelligent Systems (ISMIS-96), Zakopane,
Poland, June 10-13, 1996.
P96-17
Imam, I.F., "Do We Efficiently Estimate the Attributional
Relevancy to Learning Systems?," Proceedings of the Ninth
International Symposium on Methodologies for Intelligent Systems
(ISMIS-96), Zakopane, Poland, June 10-13, 1996.
P96-18
Duric, Z., Fayman, J.A. and Rivlin, E., "Function From
Motion," IEEE Transactions on Pattern Analysis and
Machine Intelligence, Vol. 18, No. 6, June, 1996, pp. 579-591.
P96-19
Wnek, J., Kaufman, K., Bloedorn, E. and Michalski, R.S., "Inductive
Learning System AQ15c: The Method and User's Guide," Reports
of the Machine Learning and Inference Laboratory, MLI 96-6,
George Mason University, Fairfax, VA, August, 1996.
P96-20
Bloedorn, E., Mani, I. and MacMillan, T.R., "Machine
Learning of User Profiles: Representational Issues," Proceedings
of the Thirteenth National Conference on Artificial Intelligence
(AAAI-96), Portland, OR, August, 1996.
P96-21
Kaufman, K. and Michalski, R.S., "A Method
for Reasoning with Structured and Continuous Attributes in the
INLEN-2 Knowledge Discovery System," Proceedings of
the Second International Conference on Knowledge Discovery and
Data Mining (KDD-96), Portland, OR, August, 1996, pp. 232-237.
Abstract. Structured attributes have domains (value sets) that are partially ordered sets, typically hierarchies. Such attributes allow knowledge discovery programs to incorporate background knowledge about hierarchical relationships among attribute values. Inductive generalization rules for structured attributes have been developed that take into consideration the type of nodes in the domain hierarchy (anchor or non-anchor) and the type of decision rules to be generated (characteristic, discriminant or minimal complexity). These generalization rules enhance the ability of knowledge discovery system INLEN-2 to exploit the semantic content of the domain knowledge in the process of generating hypotheses. If the dependent attribute (e.g., a decision attribute) is structured, the system generates a system of hierarchically organized rules representing relationships between the values of this attribute and independent attributes. Such a situation often occurs in practice when the decision to be assigned to a situation can be at different levels of abstraction (e.g., this is a liver disease, or this is a liver cancer). Continuous attributes (e.g., physical measurements) are quantized into a hierarchy of values (ranges of values arranged into different levels). These methods are illustrated by an example concerning the discovery of patterns in world economics and demographics.
P96-22
Duric, Z. and Rosenfeld, A., "Image Sequence Stabilization
in Real Time," Real-Time Imaging, Vol. 2, pp. 271-284,
1996.
P96-23
Bloedorn, E.E., "Multistrategy Constructive Induction,"
Ph.D. Dissertation, School of Information Technology and
Engineering, Reports of the Machine Learning and Inference
Laboratory, MLI 96-7, George Mason University, Fairfax, VA,
1996.
P96-24
Maloof, M.A. and Michalski, R.S., "Partial Memory Learning
System AQ-PM: The Method and User's Guide," Reports of
the Machine Learning and Inference Laboratory, MLI 96-8,
George Mason University, Fairfax, VA, 1996.
P96-25
Maloof, M.A., "Progressive Partial Memory Learning," Ph.D.
Dissertation, School of Information Technology and
Engineering, Reports of the Machine Learning and Inference
Laboratory, MLI 96-9, George Mason University, Fairfax, VA,
1996.
P96-26
Lee, S.W., "Multistrategy Learning: An Empirical Study with
AQ + Bayesian Approach," Reports of the Machine Learning
and Inference Laboratory, MLI 96-10, George Mason University,
Fairfax, VA, 1996.
P96-27
Lee, S.W., "WWW-AQ: World Wide Web Interface for the AQ
Learning System User's and Programmer's Guide" Reports of
the Machine Learning and Inference Laboratory, MLI 96-11,
George Mason University, Fairfax, VA, 1996.
P96-28
Zhang, Q., Duric Z. Maloof, M.A. and Michalski, R.S., "Target
Detection in SAR Images Using the MIST/AQ Method" Reports
of the Machine Learning and Inference Laboratory, MLI 96-12,
George Mason University, Fairfax, VA, 1996.
P97-1
"Publication List of the Machine Learning and Inference
Laboratory: 1988-1997," Reports of the Machine Learning
and Inference Laboratory, MLI 97-1, George Mason University,
Fairfax, VA, 1997.
P97-2
Maloof, M.A. and Michalski, R.S., "Learning Symbolic Descriptions of
Shape for Object Recognition In X-Ray Images," Expert
Systems with Applications, 12(1), 11-20, 1997.
P97-3
Michalski, R.S. and Kaufman, K.A., "Data Mining
and Knowledge Discovery: A Review of Issues and a Multistrategy
Approach," Reports of the Machine Learning and
Inference Laboratory, MLI 97-2, George Mason University,
Fairfax, VA, 1997.
Abstract. An enormous proliferation of databases in almost every area of human endeavor has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. In efforts to satisfy this need, researchers have been exploring ideas and methods developed in machine learning, pattern recognition, statistical data analysis, data visualization, neural nets, etc. These efforts have led to the emergence of a new research area, frequently called data mining and knowledge discovery. The first part of this paper is a compendium of ideas on the applicability of symbolic machine learning methods to this area. The second part describes a multistrategy methodology for conceptual data exploration, by which we mean the derivation of high-level and descriptions from data through symbolic reasoning involving both data and background knowledge. The methodology, which has been implemented in the INLEN system, combines machine learning, database and knowledge-based technologies. To illustrate the system's capabilities, we present results from its application to a problem of discovery of economic and demographic patterns in a database containing facts and statistics about the countries of the world. The presented results demonstrate a high potential utility of the methodology for assisting in solving practical data mining and knowledge discovery tasks.
P97-4
Kaufman, K.A. and Michalski, R.S.,
"KGL: A Language for
Learning," Reports of the Machine Learning and Inference
Laboratory, MLI 97-3, George Mason University, Fairfax, VA,
1997.
Abstract. In real-life data mining endeavors, the extraction of important knowledge may require many trials and errors, and the execution of sequences of data mining operations. Real world applications may pose such tasks as determining a characteristic description or a discriminant description of some classes of entities, optimizing an initial hypothesis according to a cost function, determining the most relevant attributes for a given task, selecting the most representative examples from a large example set, conceptually clustering cases into classes, predicting the class membership of a new example, generating a decision structure, automatically determining a learning curve, etc. The application of these sequences of programs can be time-consuming, laborious, and error prone. In response to these challenges, it is important for machine learning to develop a methodology for integrating diverse learning strategies so that a learning system can pursue different learning tasks and acquire different kinds of knowledge, depending on the problem at hand. We have developed a high-level language, called KGL, in which a data analyst can plan data-mining and knowledge discovery experiments using various operators for performing learning and discovery tasks. The presented approach implements a range of learning and knowledge processing programs as KGL operators. Using KGL, a user can specify a plan for applying these operators in a flexible and interdependent manner in pursuit of a desirable solution. The methodology and language are illustrated by a problem of detecting demographic and economic patterns from a database of 190 countries. The results show a great potential of the proposed approach for data mining.
P97-5
Lee, S. W., Fischthal, S., and Wnek J., "Using Bayesian
Classification for AQ-based Learning with Constructive Induction,"
Reports of the Machine Learning and Inference Laboratory,
MLI 97-4, George Mason University, Fairfax, VA, 1997.
P97-6
Zhang, Q., and Michalski, R. S., "Speeding GA-based
Attribute Selection for Image Interpretation," Reports of
the Machine Learning and Inference Laboratory, MLI 97-5 ,
George Mason University, Fairfax, VA, 1997.
P97-7
Michalski, R. S. and Imam, I. F., "On Learning
Decision Structures," Fundamenta Matematicae, 31(1),
dedicated to the memory of Dr. Cecylia Raucher, Polish Academy of
Sciences, pp. 49-64, 1997.
P97-8
Michalski, R. S., Bratko, I., and Kubat, M. (Eds.) Machine
Learning and Data Mining: Methods and Applications, London,
John Wiley & Sons, 1998.
P97-9
Lee, S. W. and Michalski, R. S., "ALPE: A System for
Automatic Learning Performance Evaluation The Method and User's
Guide," Reports of the Machine Learning and Inference
Laboratory, MLI 97-6, George Mason University, Fairfax, VA,
1997.
P97-10
Bloedorn, E. and Michalski, R.S., "Data-Driven Constructive
Induction: A Methodology and its Applications," Reports
of the Machine Learning and Inference Laboratory, MLI 97-7,
George Mason University, Fairfax, VA, 1997.
P97-11
Kaufman K.A. and Michalski, R.S., "EMERALD 2: An Integrated
System of Machine Learning and Discovery Programs for Education
and Research, User's Guide (Updated Edition)," Reports of
the Machine Learning and Inference Laboratory, MLI 97-8,
George Mason University, Fairfax, VA, 1997.
P97-12
Kaufman K.A. and Michalski, R.S., "EMERALD 2: An Integrated System of
Machine Learning and Discovery Programs for Education and
Research, Programmer's Guide for the Sun Workstation (Updated
Edition)," Reports of the Machine Learning and
Inference Laboratory, MLI 97-9, George Mason University,
Fairfax, VA, 1997.
P97-13
Fischthal, S., "A Description and User's Guide for CLUSTER/2C++
A Program for Conjunctive Conceptual Clustering," Reports
of the Machine Learning and Inference Laboratory, MLI 97-10,
George Mason University, Fairfax, VA, 1997.
P97-14
Michalski, R. S. and Zhang Q. (Eds.), "An Overview of
Research Activities in the Machine Learning and Inference
Laboratory: 1996-1997," Reports of the Machine Learning
and Inference Laboratory, MLI 97-11, George Mason University,
Fairfax, VA, 1997.
P97-15
Michalski, R. S. and Wnek, J. (Guest Editors), "Second
Special Issue on Multistrategy Learning," Machine
Learning, Vol. 27, No. 3, June 1997.
P97-16
Michalski, R. S. and Kaufman, K. A., "Multistrategy
Data Exploration Using the INLEN System: Recent Advances,"
Sixth International Conference on Intelligent Information
Systems, Zakopane, Poland, June, 1997.
P97-17
Michalski, R. S., Rosenfeld, A., Aloimonos, Y., Duric, Z., Maloof,
M., and Zhang, Q. "Computer Vision through Learning," Reports
of the Machine Learning and Inference Laboratory , MLI 97-12,
George Mason University, Fairfax, VA, 1997.
P97-18
Li, Z., Kafatos, M. and Michalski, R.S., "El Nino
Teleconnections Research: Initial Results Using a Machine
Learning and Discovery Approach," Reports of the Machine
Learning and Inference Laboratory , MLI 97-13, George Mason
University, Fairfax, VA, 1997.
P97-19
Zhang, Q., "Knowledge Visualizer: A Software
System for Visualizing Data, Patterns and Their Relationships,"
Reports of the Machine Learning and Inference Laboratory , MLI
97-14, George Mason University, Fairfax, VA, September, 1997.
P97-21
Michalski, R.S., "Seeking Knowledge in the Deluge of Facts,"
Fundamenta Informaticae, Vol. 30, pp. 283-297, 1997.
P97-22
Kaufman, K.A., "INLEN: A
Methodology and Integrated System for Knowledge Discovery in
Databases," Ph.D. Dissertation, School of
Information Technology and Engineering, Reports of the Machine
Learning and Inference Laboratory, MLI 97-15, George Mason
University, Fairfax, VA, November, 1997.
P97-23
Zhang, Q. and Michalski, R.S., "An Easy Evaluation Program
for AQ Learning Programs," Reports of the Machine
Learning and Inference Laboratory, MLI 97-16, George Mason
University, Fairfax, VA, December, 1997.
P98-1
Michalski, R. S. and Zhang, Q. (Eds.), "An Overview of
Research Activities in the Machine Learning and Inference
Laboratory: 1997-1998," Reports of the Machine Learning
and Inference Laboratory, MLI 98-1, George Mason University,
Fairfax, VA, January, 1998.
P98-2
Zhang, Q., Duric Z., and Michalski, R.S., "Detecting
Targets in in SAR images: a Machine Learning Approach," Proceedings
of the Third Asian Conference on Computer Vision, Hong Kong,
January 1998.
P98-3
Fischthal, S., "Conceptual Clusterer CLUSTER/2C++: An Object-Oriented
Design and Code Documentation," Reports of the Machine
Learning and Inference Laboratory, MLI 98-2, George Mason
University, Fairfax, VA, 1998.
P98-4
Kubat, M., Bratko, I. and Michalski, R.S., "A Review of Machine Learning Methods,"
in Michalski, R.S., Bratko, I. and Kubat, M. (Eds.), Machine
Learning and Data Mining: Methods and Applications, London:
John Wiley & Sons, pp. 3-69, 1998.
P98-5
Michalski, R.S. and Kaufman, K.A., "Data Mining
and Knowledge Discovery: A Review of Issues and a Multistrategy
Approach," in Michalski, R.S., Bratko, I. and Kubat, M.
(Eds.), Machine Learning and Data Mining: Methods and
Applications, London: John Wiley & Sons, pp. 71-112, 1998.
P98-6
Michalski, R.S., Rosenfeld, A., Duric, Z., Maloof, M.A. and Zhang,
Q., "Learning Patterns in Images,"
in Michalski, R.S., Bratko, I. and Kubat, M. (Eds.), Machine
Learning and Data Mining: Methods and Applications, London:
John Wiley & Sons, pp. 241-268, 1998.
P98-7
Michalski, R.S, and Zhang, Q., "An Application of Lamarckian
Evolution Model to Function Optimization," Reports of the
Machine Learning and Inference Laboratory, MLI 98-3, George
Mason University, Fairfax, VA, 1998.
P98-8
Bloedorn, E. and Michalski, R.S., "Data-Driven
Constructive Induction," IEEE Intelligent Systems,
Special issue on Feature Transformation and Subset Selection, pp.
30-37, March/April, 1998.
P98-9
Michalski, R.S., "Learnable Evolution: Combining Symbolic
and Evolutionary Learning," Proceedings of the Fourth
International Workshop on Multistrategy Learning (MSL'98),
Desenzano del Garda, Italy, pp. 14-20, June 11-13, 1998.
P98-10
Kaufman, K.A. and Michalski, R.S., "Discovery
Planning: Multistrategy Learning in Data Mining," Proceedings
of the Fourth International Workshop on Multistrategy Learning (MSL'98),
Desenzano del Garda, Italy, June 11-13, 1998.
P98-11
Esposito, F., Michalski, R.S., and Saitta, L. (Eds.), Proceedings
of the Fourth International Workshop on Multistrategy Learning (MSL'98),
Desenzano del Garda, Italy, June 11-13, 1998.
P98-12
Kaufman, K.A. and Michalski, R.S.,
"Multistrategy
Data Mining via the KGL Metalanguage," Proceedings of
the Seventh Symposium on Intelligent Information Systems (IIS'98),
Malbork, Poland, pp. 39-48, June 15-19, 1998.
P99-1
Michalski, R. S. and Kaufman, K. (Eds.), "An Overview of
Research Activities in the Machine Learning and Inference
Laboratory: 1998-1999," Reports of the Machine Learning
and Inference Laboratory, MLI 99-1, George Mason University,
Fairfax, VA, January, 1999.
P99-2
Kaufman, K.A. and Michalski, R.S.,
" Learning in an
Inconsistent World: Rule Selection in AQ18," Reports
of the Machine Learning and Inference Laboratory, MLI 99-2,
George Mason University, Fairfax, VA, May, 1999.
P99-3
Michalski, R.S.,
"LEARNABLE EVOLUTION MODEL:
Evolutionary Processes Guided by Machine Learning," subsumed by ML Journal
paper, Reports of the Machine Learning and Inference Laboratory, MLI 99-3,
George Mason University, Fairfax, VA, May, 1999.
P99-4
Michalski. R.S. and Zhang, Q., "Initial Experiments with the LEM1 Learnable
Evolution Model: An Application to Function Optimization and Evolvable
Hardware," Reports of the Machine Learning and Inference Laboratory,
George Mason University, MLI 99-4, George Mason University, Fairfax, VA,
May 1999.
P99-5
Coletti, M., Lash, T., Mandsager, C., Michalski, R. S., and Moustafa, R.,
"An Experimental Application of the Learnable
Evolution Model System LEM1 and Genetic Algorithms GA1 and GA2 to Parameter
Identification in Digital Signal Filter Design," Reports of the
Machine Learning and Inference Laboratory, MLI 99-5, George Mason
University, Fairfax, VA, May 1999.
P99-6
Cervone, G.,
"
The LEM2 Implementation of Learnable Evolution Model and Its
Testing on Selected Optimization Problems," Reports of the Machine
Learning and Inference Laboratory, MLI 99-6, George Mason University,
Fairfax, VA, May 1999.
P99-7
Kaufman, K.A. and Michalski, R.S., "Learning from
Inconsistent and Noisy Data: The AQ18 Approach," Proceedings of the
Eleventh International Symposium on Methodologies for Intelligent Systems,
Warsaw, pp. 411-419, June 8-11, 1999.
P99-8
Michalski, R.S. and Kaufman, K.A.,
"Discovering Multidimensional Patterns in
Large Datasets Using Knowledge Scouts," Reports of the Machine Learning
and Inference Laboratory, MLI 99-7, George Mason University, Fairfax, VA,
June 1999.
P99-9
Maloof, M. and Michalski, R.S.,
"AQ-PM: A Method for Partial Memory Learning," Proceedings of the Eighth
Symposium on Intelligent Information Systems, Ustron, Poland, pp. 70-79,
June, 1999.
P99-10
Michalski, R.S. and Kaufman, K.A.,
"A Measure of Description Quality
for Data Mining and its Implementation in the AQ18 Learning System,"
Proceedings of the ICSC Congress on Computational Intelligence
Methods and Applications (CIMA-99), Rochester, NY, pp. 369-375, June,
1999.
P99-11
Coletti, M., Lash, T., Mandsager, C., Michalski, R.S., and Moustafa, R.,
"Comparing Performance of the Learnable Evolution Model and Genetic
Algorithms on Problems in Digital Signal Filter Design," Proceedings
of the 1999 Genetic and Evolutionary Computation Conference (GECCO),
Orlando, July, 1999.
P99-12
Cervone, G., "An Experimental Application of the Learnable Evolution Model to
Selected Optimization Problems," Master's Thesis, Department of Computer
Science, Reports of the Machine Learning and Inference Laboratory,
MLI 99-12, George Mason University, Fairfax, VA, November 1999.
P00-1
P00-2
Michalski, R.S., "LEARNABLE EVOLUTION MODEL:
Evolutionary Processes Guided by Machine Learning," Machine
Learning 38, pp. 9-40, 2000.
P00-3
Kaufman, K.A. and Michalski, R.S.,
"ISHED1: Applying the LEM
Methodology to Heat Exchanger Design," Reports of the Machine Learning
and Inference Laboratory, MLI 00-2, George Mason University, Fairfax,
VA, 2000.
P00-4
Kaufman, K.A. and Michalski, R.S., "The AQ18 System
for Machine Learning: User's Guide," Reports of the Machine Learning
and Inference Laboratory, MLI 00-3, George Mason University, Fairfax, VA,
2000.
P00-5
Kaufman, K.A. and Michalski, R.S.,
"An Adjustable Rule Learner
for Pattern Discovery Using the AQ Methodology," Journal of
Intelligent Information Systems, 14, pp. 199-216, 2000.
P00-6
Michalski, R.S. and Kaufman, K., "Building Knowledge
Scouts Using KGL Metalanguage," Fundamenta Informaticae 40,
pp. 433-447, 2000.
P00-7
Cervone, G., Michalski, R.S., Kaufman, K. and Panait, L.,
" Combining Machine Learning with Evolutionary
Computation: Recent Results on LEM," Proceedings of the Fifth
International Workshop on Multistrategy Learning (MSL-2000),
Guimarães, Portugal, pp. 41-58, June, 2000.
P00-8
Michalski, R.S., Cervone, G. and Kaufman, K., "
Speeding Up Evolution through Learning: LEM," Proceedings of the Ninth
International Symposium on Intelligent Information Systems, Bystra,
Poland, June 12-16, 2000.
P00-9
Cervone, G., Kaufman, K. and Michalski, R.S., "
Experimental Validations of the Learnable Evolution Model," 2000
Congress on Evolutionary Computation, San Diego CA, pp. 1064-1071,
July, 2000.
P00-10
Kaufman K. and Michalski, R.S.,
"Applying Learnable Evolution
Model to Heat Exchanger Design," Proceedings of the Seventeenth National
Conference on Artificial Intelligence (AAAI-2000) and Twelfth Annual
Conference on Innovative Applications of Artificial Intelligence
(IAAI-2000), Austin, TX, pp. 1014-1019, 2000.
P00-11
Maloof, M. and Michalski, R.S.,
"Selecting Examples for Partial
Memory Learning," Machine Learning (41), pp. 27-52, 2000.
P00-12
Kaufman, K.A. and Michalski, R.S.,
"A Knowledge Scout for Discovering
Medical Patterns: Methodology and System SCAMP," Proceedings of the Fourth
International Conference on Flexible Query Answering Systems, FQAS'2000,
Warsaw, Poland, pp. 485-496, October 25-28, 2000.
P00-13
Michalski, R.S., "Learning and Evolution: An
Introduction to Non-Darwinian Evolutionary Computation," Invited
paper, Twelfth International Symposium on Methodologies for Intelligent
Systems, Charlotte, NC, 2000.
P01-1
P01-2
Michalski, R.S. and Cervone, G., "Adaptive
Anchoring Quantization of Continuous Variables for Learnable Evolution,"
Reports of the Machine Learning and Inference Laboratory, MLI 01-3,
George Mason University, Fairfax, VA.
P01-3
Cervone, G., Panait, L.A. and Michalski, R.S.,
"The Development of the AQ20 Learning System and Initial
Experiments," Tenth
International Symposium on Intelligent Information Systems, Zakopane,
Poland, June 2001.
P01-4
Glowinski, C. and Michalski, R.S., "Discovering
Multi-head Attributional Rules in Large Databases," Tenth
International Symposium on Intelligent Information Systems, Zakopane,
Poland, June 2001.
P01-5
Michalski, R.S., "Attributional Calculus: A
Representation System and Logic for Deriving Human Knowledge from Computer
Data," Reports of the Machine Learning and Inference Laboratory,
MLI 01-1, George Mason University, Fairfax, VA, 2001.
To appear:
Cervone, G., "EMERALD-PC/AQ: A PC-Based Implementation of the AQ Module in the
EMERALD System," Reports of the Machine Learning and Inference
Laboratory, George Mason University, Fairfax, VA.
Cervone, G., Coletti, M. and Latiner, R., "ECC++: A Generic C++ Library for Evolutionary Computation ," Reports of the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA.
Cervone, G. and Michalski, R.S., "Design and Experiments: LEM2 Implementation of the Learnable Evolution Model," Reports of the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA.
Lattner, A. "Integration of Picture Finding Segmentation Algorithms to the MIST Methodology," Reports of the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA.
Maloof, M. and Michalski, R.S., "Learning Symbolic Descriptions of Shape for Object Recognition of X-Ray Images," Intelligent Systems.
Michalski, R.S. and Kaufman, K, "Learning Patterns in Noisy Data: The AQ Approach," in Paliouras, G., Karkaletsis, V. and Spyropoulos (eds.) Machine Learning and Applications, Springer-Verlag, 2001.