Seeking knowledge in the deluge of facts

Fundamenta Informaticae

Volumn 30, Issue 3-4, 1997, Pages 283-297

  Michalski, Ryszard S ^a,b  

^a GEORGE MASON UNIVERSITY   (United States)

^b POLISH ACADEMY OF SCIENCES   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; RELATIONAL DATABASE SYSTEMS; USER INTERFACES;

KNOWLEDGE GENERATION OPERATORS;

KNOWLEDGE BASED SYSTEMS;

EID: 0031165377     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: 10.3233/fi-1997-303405     Document Type: Article

References (38)

1. Bergadano, F., Matwin, S., Michalski, R.S., and Zhang, J., "Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System," Reports of Machine Learning and Inference Laboratory, MLI 90-9, George Mason University, Fairfax, VA September 1990.
2. 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 9th International Symposium on Methodologies for Intelligent Systems, June 9-13, Zakopane,1996.
3. Bongard, N., Pattern Recognition, Spartan Books, New York,1970 (a translation from Russian).
4. Brachman, R., Selfridge, P., Terveen, L., Altman, B., Halper, F., Kirk, T., Lazar, A., McGuiness, D., Resnick, L., and Borgida, A., "Integrated Support for Data Archeology," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp.197-211, Washington D.C., 1993.
5. Carbone, P., and Kerschberg, L., "Intelligent Mediation in Active Knowledge Mining: Goals and General Description," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp. 241-253, Washington D.C.,1993.
6. Chan, P., and Stolfo, S., "Toward Parallel and Distributed Learning by Meta-Learning," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp. 227-240, Washington D.C.,1993.
7. Cheng, P.C.-H and Simon, H.A., "The Right Representation for Discovery: Finding the Conservation of Momentum," Proceedings of the Ninth International Conference on Machine Learning, Aberdeen Scotland, pp. 62-71,1992.
8. Cooper, G., "A Bayesian Method for Learning Probabilistic Networks that Contain Hidden Variables," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp.112-124, Washington D.C., 1993.
9. Dzeroski, S., "Inductive Logic Programming for KDD: an Overview," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp. 125-137, Washington D.C., 1993.
10. Fayyad U. and Uthurusamy, R., The First International Conference on Knowledge Discovery and Data Mining, Montreal, Canada, August 20-21, 1995.
11. Forsyth, R. and Rada, Roy, Machine Learning: applications in expert systems and information retrieval, Pitman, 1986.
12. Imam, I.F., Michalski, R.S., and Kerschberg, L., "Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Techniques," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp. 264-275, Washington D.C., 1993.
13. 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, Vol. 2, pp. 279-304, 1993.
14. Kaufman, K.A., Michalski, R.S. and Schultz, A.C., 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 89-11, George Mason University, Fairfax, VA,1989.
15. Kaufman, K.A., Michalski, R.S., and Kerschberg, L., AN ARCHITECTURE FOR Knowledge Discovery from Facts: Integrating Database, Knowledge Base and Machine Learning in INLEN, in Machine Learning and Inference Reports, 1991.
16. Kaufman, K.A., Michalski, R.S., A Multistrategy Conceptual Analysis of Economic Data, Proceedigns of the 4th International Workshop on Artificial Intelligence in Econoics and Management, Tel-Aviv, Isreal, January 8-10, 1996.
17. Kloesgen, W., "Some Implementation Aspects of a Discovery System," Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp. 212-226, Washington D.C., 1993.
18. Kodratoff, Y., Introduction to Machine Learning, Pittman, 1988.
19. Klopotek, M., Michalewicz M., Wierzchon, S., Ekstracja Wiedzy w Sieci Bayesowskiej, Materialy Konferencji Naukowej Praktyczne Aspekty Sztucznej Inteligeacji, Instytut Podstaw Informatyki, Polska Akademia Nauk (Proceedings of the Polish conference on the Practical Aspects of Artificial Intelligence), Zakopane 7-11, 12, 1992.
20. Major, J., and Mangano, J., "Selecting Among Rules Induced from a Hurricane Database," Proceedings of the AAAI-93 Workshop on Knowdedge Discovery in Databases, pp. 28-44, Washington D.C., 1993.
21. Michalski, R.S., "A Planar Geometrical Model for Representing Multi-Dimensional Discrete Spaces and Multiple-Valued Logic Functions," Report No. 897, Department of Computer Science, University of Illinois, Urbana, January 1978.
22. Michalski, R. S., Stepp, R. and Diday, E., "A Recent Advance in Data Analysis: Clustering Objects into Classes Characterized by Conjunctive Concepts," Invited chapter in the book Progress in Pattern Recognition, Vol.1, L. Kanal and A. Rosenfeld (Editors), pp. 33-55., 1981.
23. Michalski, R.S., "A Theory and Methodology gf Inductive Learning," Artificial Intelligence, 1983,. pp.111-161, 1983.
24. Michalski, R.S., "Learning Flexible Concepts: Fundamental Ideas and a Method Based on Two-tiered Representation," Machine Learning: An Artificial Intelligence Approach, Vol. III, Y. Kodratoff and R.S. Michalski (Eds.), Morgan Kaufmann Publishers, 1990.
25. Michalski, "Inferential Theory of Learning: Developing Foundations for Multistrategy Learning, Machine Learnin: A Multistrate Learning Approach Vol. 4. Morgan Kaufmann Publishers 1994.
26. Michalski, R.S., Kerschberg, L., Kaufman, K, and Ribeiro, J., "Mining for Knowledge in Databases: The INLEN Architecture, Initial Implementation and First Results," Journal of Intelligent Information Systems, pp. 85-113, Vol.1, No.1, August, 1992.
27. Michalski, R.S. and Stepp, R., "Learning from Observation: Conceptual Clustering," Machine Learning: An Artificial Intelligence Approach, Morgan Kaufman, 1993.
28. Michalski, R.S. and Kaufman, K., "Multistrategy Data Minining and Data Analysis," chapter in Machine Learning and Data Mining, R.S. Michalski, I. Bratko and M. Kubat (eds.), John Wiley & Sons, 1997 (in press).
29. Michie, D., "Methodologies from Machine Learning in Data Analysis and Software," The Computer Journal, vol.34, Noi. 6, 1991.
30. Pawlak, Z., Rough Sets-Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, 1991.
31. Piatetsky-Shapiro, G. (ed.), Proceedings of the AAAI-91 Workshop on Knowledge Discovery in Databases, 1991.
32. Piatetsky-Shapiro, G. (ed.), Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, 1993.
33. Quinlan, J.R., "Induction of Decision Trees," Machine Learning, Vol.1, No.1, pp. 81-106, 1986.
34. Wnek, J., Sarma, J., Wahab, A., 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, George Mason University, Fairfax, VA, January 1990.
35. Wnek, J. and Michalski, R.S., "Hypothesis-driver Constructive Induction in AQl7-HCI: A Method and Experiments," Machine Learning, Special Issue on Evaluating and Changing Representations, 1993.
36. Ziarko, W. P., Rough sets, fuzzy sets and knowledge discovery, Proceedings of the International Workshop RSKD'94, Banff, Alberta, Canada, October 12-15, 1993.
37. Zembowicz, R. and Zytkow, J.M., "Discovery of Equations: Experimental Evaluation of Convergence," Proceedings of AAAI-92, San Jose, CA, pp. 70-73, 1992.
38. Zytkow, J.M., Zhu, J. and Zembowicz, R., "The First Phase of Real-World Discove Determining Repeatability and Error of Experiments," Proceedings of the Ninth International Conference on Machine Learning, Aberdeen Scotland, pp. 480-485, 1992.