- An invited keynote talk at the Fifth International Symposium on Applied Stochastic Models and Data Analysis
- Learning Problem-Oriented Decision Structures from Decision Rules: The AQDT-2 System
- Invited talk at the Sanken Symposium on Data Mining and Semantic Web, Osaka University, Japan, March 10-11, 2003
- Speeding Up Evolution through Learning: LEM Ryszard Michalski*, Guido Cervone and Kenneth Kaufman
- Proceedings of the Second International Workshop MULTISTRATEGY LEARNING
- Natural Language Understanding by Computer The Next Step
- BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES
- Knowledge Visualization Using Optimized General Logic Diagrams
- Intelligent Optimization via Learnable Evolution Model Ryszard S. Michalski, Janusz Wojtusiak, and Kenneth A. Kaufman
- INLEN: A Methodology and Integrated System for Knowledge Discovery in Databases
- Appears in Z Ras (ed), Lecture Notes in Computer Science, Vol. 0869, Springer-Verlag, 1997; also appears in the Proceedings afthe 10th
- A Rules-to-Trees Conversion in the Inductive Database System VINLEN
- User Manual Specification and Guide through
- Machine Learning and Inference Laboratory Reasoning with Meta-values in AQ Learning
- Addressing Knowledge Discovery Problems in a Multistrategy Framework Kenneth A. Kaufman
- Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Ibrahim F. Imam and Janusz Wnek (Eds.), pp. 38-51, Melbourne Beach, Florida, 1995.
- Machine Learning and Inference Laboratory AQ21 User's Guide
- Machine Learning and Inference Laboratory The Distribution Approximation Approach to
- Machine Learning and Inference Laboratory Initial Considerations toward Knowledge Mining
- Machine Learning and Inference Laboratory Validating Learnable Evolution Model on
- Machine Learning and Inference Laboratory Attributional Ruletrees
- Machine Learning and Inference Laboratory ATTRIBUTIONAL CALCULUS
- The AQ18 Machine Learning and Data Mining System
- Machine Learning and Inference Laboratory An Integrated System of Machine Learning
- Data Mining and Knowledge Discovery: A Review of Issues and
- Reports of Machine Learning and Inference Laboratory, MLI 94-7, Center for MLI, George Mason Univ., December 1994. CONCEPTUAL TRANSITION FROM LOGIC TO ARITHMETIC IN
- CONSTRUCTIVE INDUCTION FROM DATA IN AQ17-DCI: Further Experiments
- A shorter version of this report is to be submitted to Cognitive Science. .A Validation and Exploration of
- The Natural Induction System AQ21 and Its Application to Data Describing Patients with Metabolic Syndrome: Initial Results
- The Use of Compound Attributes in AQ Janusz Wojtusiak1
- Learning and Evolution: An Introduction to Non-Darwinian Evolutionary Computation
- Proceedings of the Fifth International Workshop on Multistrategy Learning (MSL-2000), Guimares, Portugal, pp. 41-58, June, 2000.
- Fundamenta Informaticae 40, pp. 433-447, 2000. Building Knowledge Scouts
- Category: Genetic Algorithms Comparing Performance of the
- 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.
- Appears in R. Chin and T.C. Pong (eds), Lecture Notes in Computer Science Vol. 1351, Springer-Verlag, 1997; also appears in the
- Machine Learning and Inference Laboratory Learning User Models
- An Integrated System of Machine Learning and Discovery Programs to Support AI Education and Experimental Research
- Discovery Planning: Multistrategy Learning in Data Mining Kenneth A. Kaufman and Ryszard S. Michalski *
- The CLUSTER3 system for goal-oriented conceptual clustering: method and preliminary
- Proceedings of the Second World Conference on the Fundamentals of Artificial Intelligence
- ISHED1: Applying the LEM Methodology to Heat Exchanger Design
- An extended and improved version ofthe invited paper published in the
- Proceedings of the Third International Round-Table Conference on Computational Models of Creative Design, Heron Island, Queensland, Australia, December 3-7, 1995.
- Machine Learning and Inference Laboratory Adaptive Anchoring Discretization for
- Machine Learning and Inference Laboratory An Application of Symbolic Learning to
- The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems
- Machine Learning and Inference Laboratory Multitype Pattern Discovery via AQ21
- In Machine Learning: A Multistrategy Approach, Volume 4 R.S. Michalski & G. Tecuci (Eds.), Morgan Kaufmann Publishers, 1993.
- Learning Patterns in Noisy Data: The AQ Approach Ryszard S. Michalski* and Kenneth A. Kaufman
- From Data Mining to Knowledge Mining Kenneth A. Kaufman and Ryszard S. Michalski
- \IEIl. ,'Hunt. (1983), \'ot. 8, :"-0. 3, 187-195 A logic-based approach to conceptual data base analysis
- Machine Learning, 38, 940, 2000. c 2000 Kluwer Academic Publishers. Printed in The Netherlands.
- In Proceedings oCTIle 4th European Working Session onL...eaming December.. 1989
- This paper presents a method for constructive induction, in which new attributes are constructed
- LEARNING DESIGN RULES FOR WIND BRACINGS IN TALL Tomasz Arciszewski1, Associate Member ASCE, Eric Bloedorn2
- The Fourth Workshop on Intelligent Information Systems (WIS `95), Augustow, Poland, June 5-10, 1995.
- The MIST Methodology and Its Application to Natural Scene Interpretation1
- Proceedings of the Ninth International Symposium on Methodologies for Intelligent Systems (ISMIS-96), Zakopane, Poland, June 10-13, 1996
- Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland OR, August 2-4, 1996, pp. 232-237
- To the special memory of Cecylia Rauszer--an outstanding scientist, a magnanimous human being, and a dear friend.
- Multistrategy Data Exploration Using the INLEN System: Recent Advances
- Journal of Intelligent Information Systems, Vol. 14, pp. 199-216, 2000 An Adjustable Description Quality Measure for Pattern
- 2000 Congress on Evolutionary Computation, San Diego CA, July, 2000 Experimental Validations of the Learnable Evolution Model
- Proceedings of the Fourth Int'l Conference on Flexible Query Answering Systems, FQAS 2000, Warsaw, Poland, pp. 485-496, October, 2000.
- Discovering Multi-head Attributional Rules in Large Databases
- Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results
- Presented at the UQM Summer Institute in Cognitive Sciences, Montreal, June 30th-July 11th, 2003.
- To appear in the Proceedings of the 2nd International Workshop on Multistrategy Learning, Harpers Ferry, VW, May, 1993 Multistrategy Constructive Induction
- An Integrated Multi-task Inductive Database and Decision Support System VINLEN: An Initial
- The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features
- Applying Learnable Evolution Model to Heat Exchanger Design Kenneth A. Kaufman and Ryszard S. Michalski*
- International Journal of Intelligent Systems Volume 21, Issue 12, 2006
- LEARNING IN AN INCONSISTENT WORLD: Rule Selection in AQ18
- A Teehnieal Deaeription of the Meta-expert System
- Hypothesis.driven Constructive Induction in AQ17
- Reports of the GMU Machine Learning and Inference Laboratory, MLI 95-3, March 1995. HOW DID AQ FACE THE EAST-WEST CHALLENGE?
- KGL: A Language for Learning Kenneth A. Kaufman
- An Integrated System of Machine Learning and Discovery Programs
- An Easy Performance Evaluation Program for AQ Learning Programs
- An Application of Lamarckian Evolution Model to Function Optimization
- Machine Learning and Inference Laboratory Initial Experiments with the LEM1 Learnable Evolution Model
- An Experimental Application of Learnable Evolution Model and Genetic Algorithms
- Discovering Multidimensional Patterns in Large Datasets by Knowledge Scouts
- Machine Learning and Inference Laboratory The AQ19 System for Machine Learning
- Machine Learning and Inference Laboratory An Optimized Design of Finned-Tube Evaporators
- Machine Learning and Inference Laboratory Generating Alternative Hypotheses in AQ Learning
- Machine Learning and Inference Laboratory The LEM3 System for Non-Darwinian Evolutionary
- Reasoning with Missing, Not-applicable and Irrelevant Meta-values in Concept Learning and
- Machine Learning and Inference Laboratory Natural Induction and Conceptual Clustering
- Machine Learning and Inference Laboratory Semantic and Syntactic Attribute Types
- Machine Learning and Inference Laboratory Progress Report on Learnable Evolution Model
- Machine Learning and Inference Laboratory The LEM3 Implementation of
- Knowledge Visualizer: a Software System for Visualizing Data, Patterns
- Machine Learning of User Profiles: Representational Issues +*Eric Bloedorn, +Inderjeet Mani, and +T. Richard MacMillan
- Genetic Algorithms as a Tool for Restructuring Feature Space Representations
- Proceedings of the AAAI-94 Workshop on Knowledge Discovery in Databases, Seattle, WA, pp. 229-236, August, 1994. COMPARING INTERNATIONAL DEVELOPMENT PATTERNS USING
- Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results
- Theory and Implementation of the Learnable Evolution Model
- To appear in A. Ram & D.B. Leake, editors, Goal-Driven Learning, MIT Press/Bradford Books. Learning as Goal-Driven Inference
- Submitted to Machine Learning Journal, revised vezsion (October 20th, 1989) LEARNING ATTRIBUTIONAL TWO-TIERED
- Machine Learning and Data Mining: Methods and Applications Edited by R.S. Michalski, I. Bratko and M. Kubat
- Proceedings of the International ICSC Symposium on Advanced in Intelligent Data Analysis (AIDA),
- MULTISTRATEGY CONSTRUCTIVE INDUCTION Eric E. Bloedorn
- Eleventh International Symposium on Methodologies for Intelligent Systems, Warsaw, pp. 411-419, 1999
- Ryszard Michalski Machine Learning and Inference Laboratory
- The Development of the AQ20 Learning System and Initial Experiments
- PDF-ToolBox. DEMO VERSION http://www.docu-track.com/
