Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-22T15:07:43.903Z Has data issue: false hasContentIssue false
  • English
  • Français

A unifying framework for concept-learning algorithms

Published online by Cambridge University Press:  07 July 2009

Luc de Raedt  and
Maurice Bruynooghe
Luc de Raedt
Affiliation:
Department of Computer Science, Katholleke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
Maurice Bruynooghe
Affiliation:
Department of Computer Science, Katholleke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
Get access Rights & Permissions [Opens in a new window]

Abstract

A unifying framework for concept-learning, derived from Mitchell's Generalization as Search-paradigm, is presented. Central to the framework is the generic algorithm Gencol. Gencol forms a synthesis of existing concept-learning algorithms as it identifies the key issues in concept-learning: the representation of concepts and examples, the search strategy and heuristics, and the operators that transform one concept-description into another one when searching the concept description space. Gencol is relevant for practical purposes as it offers a solid basis for the design and implementation of concept-learning algorithms. The presented framework is quite general as seemingly disparate algorithms such as TDIDT, AQ, MIS and version spaces fit into Gencol.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 3 , September 1992 , pp. 251 - 269
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adé, H, De Raedt, L and Bruynooghe, M, 1992. “Inverse resolution in an integrated inductive-deductive learning System” In: Proceedings of the lOth European Conference on Artifidal Intelligence,Wiley.Google Scholar
Angluin, D and Smith, CS, 1983. “Inductive inference: theory and methods”, ACM Computing Surveys 15 237–269.CrossRefGoogle Scholar
Biermann, A, 1986, “Fundamental mechanisms in machine learning and inductive inference” In: Bibel, W and Jorrand, P, editors, Fundamentals of Artifidal Intelligence Springer-Verlag.Google Scholar
Bratko, I, Mozetic, I and Lavrač, N, 1989. Kardio: a study in deep and qualitative knowledge for expert Systems, MIT Press.Google Scholar
Buchanan, BG and Mitchell, TM, 1978. “Model-directed learning of production rules” In: Waterman, DA and Hayes-Roth, F, editors, Pattern-directed inference Systems Academic Press.Google Scholar
Bundy, A, Silver, B and Plummer, D, 1985. “An analytical comparison of some rule-learning programs” Artifidal Intelligence 27 137–181.CrossRefGoogle Scholar
Buntine, W, 1988, “Generalized subsumption and its application to induction and redundancy” Artificial Intelligence 36 375–399.CrossRefGoogle Scholar
Causse, K, Morik, K, Sims, P and Rouveirol, C, 1990. “A comparative study of the representation languages used in the Machine Learning Toolbox” In: ESPRIT 90, Kluer.Google Scholar
Cestnik, B, Kononenko, I and Bratko, I, 1987. “Assistant 86: a knowledge-elicitation tool for sophisticated users” In Proceedings of the 2nd European Working Session on Learning, pp 31–45, Sigma Press.Google Scholar
Cohen, PR and Feigenbaum, EA, editors, 1981. The handbook of Artificial intelligence vol. 3, Morgan Kaufmann.Google Scholar
Declercq, E, 1990. Een uitwerking van het generisch algoritme Gencol dat concepten leert, Master's thesis, Department of Computer Science, Katholieke Universiteit Leuven (in Dutch).Google Scholar
De Raedt, L, 1991. Interactive Concept-Learning PhD thesis, Department of Computer Science, Katholieke Universiteit Leuven.Google Scholar
De Raedt, L, 1992. Interactive Theory Revision: an inductive logic programming approach, Academic Press.Google Scholar
De Raedt, L and Bruynooghe, M, 1992. “Interactive concept-learning and constructive induction by analogy” Machine Learning 8 107–150.CrossRefGoogle Scholar
De Raedt, L, Krekels, B, Bruynooghe, M and Van Meir, D, 1987. “Using Shapiro's model inference System for concept-learning”. In: Proceedings of the 4th international conference on Artificial Intelligence and Control Systems of Robots, pp 191–196, North Holland.Google Scholar
Dietterich, TG and Michalski, RS, 1983. “A comparative review of selected methods for learning from examples”. In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: an Artificial intelligence approach, vol. 1, Tioga Publishing Co.Google Scholar
Gams, M and Lavrač, N, 1987. “Review of five empirical learning Systems within a proposed schemata”. In Proceedings of the 2nd European Working Session on Learning pp 46–66, Sigma Press.Google Scholar
Genesereth, M and Nilsson, N, Logical foundations of artificial intelligence, Morgan Kaufmann.Google Scholar
Gold, EM, 1987. “Language identification in the limit”, Information and control 10 447–474.CrossRefGoogle Scholar
Hayes-Roth, F and McDermott, J, 1978. An interference matching technique for inducing abstractions”, Communications of the ACM 21 401–410.CrossRefGoogle Scholar
Kalkanis, G and Conroy, GV, 1991. “Principles of induction and approaches to attribute based induction”, Knowledge Engineering Review 6.CrossRefGoogle Scholar
Kocabas, S, 1991, “A review of learning” Knowledge Engineering Review 6.CrossRefGoogle Scholar
Kodratoff, Y and Michalski, RS, editors, 1990. Machine Learning: an artificial intelligence approach, Vol. 3, Morgan Kaufmann.Google Scholar
Kodratoff, Y, 1988. Introduction to Machine Learning Pitman.Google Scholar
Laird, PD, 1988. “Inductive inference by refinement”. In: Proceedings of the 5th American Conference on Artificial Intelligence pp 472–476, Morgan.Google Scholar
Langley, P, 1987. “The terminology of machine learning” Machine Learning 1 141–144.CrossRefGoogle Scholar
Langley, P and Carbonell, JG, 1987. “Machine learning”. In: Shapiro, S, editer, Encyclopedia of artificial intelligence, Wiley.Google Scholar
Langley, P, Gennari, JH, and Iba, W, 1987. “Hill-climbing theories of learning”. In: Proceedings of the 4th International Workshop on Machine Learning pp 312–323, Morgan Kaufmann.Google Scholar
Lavrač, N, Džeroski, S and Grobelnik, M, 1991, “Learning non-recursive definitions of relations with LINUS”. In: Kodratoff, Y, editor, Proceedings of the 5th European Working Session on Learning, vol. 482 of Lecture Notes in Artificial Intelligence Springer-Verlag.Google Scholar
Matheus, CJ, 1989. “A constructive induction framework”. In: Proceedings of the 6th International Workshop on Machine Learning, pp 474–475, Morgan Kaufmann.CrossRefGoogle Scholar
Mehra, P, Rendell, LA and Wah, BW, 1989. “Principled constructive induction”. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence pp 651–657, Morgan Kaufmann.Google Scholar
Michalski, RS and Chilauski, RL, 1980. “Learning by being told and learning from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert System for soybean disease diagnosis”, Policy analysis and information Systems 4.Google Scholar
Michalski, RS, Carbonell, JG and Mitchell, TM, 1983. Machine Learning: an artificial intelligence approach, Vol. 1, Morgan Kaufmann.CrossRefGoogle Scholar
Michalski, RS, Carbonell, JG and Mitchell, TM, 1986. Machine Learning: an artificial intelligence approach, Vol. 2, Morgan Kaufmann.Google Scholar
Michalski, RS, Davis, JH, Bisht, VS and Sinclair, JB, 1985. “Plant/ds: an expert consulting System for the diagnosis of soy-bean diseases”. In: Steels, L and Campbell, JA, editors, Progress in Artificial Intelligence Ellis Horwood.Google Scholar
Michalski, RS, 1975. “Variable-valued logic and its applications to pattern recognition and machine learning”. In: Rine, D, editor, Multiple-Valued Logic and Computer Science North-Holland.Google Scholar
Michalski, RS, 1983. “A theory and methodology of inductive learning”. In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: an artificial intelligence approach, vol 1, Morgan Kaufmann.CrossRefGoogle Scholar
Michalski, RS, 1987. “Clustering”. In: Shapiro, S, editor, Encyclopedia of artificial intelligence Wiley.Google Scholar
Morik, K, 1989. “Sloppy modeling”. In: Morik, S, editor, Knowledge Representation and Organization in Machine Learning vol. 347 of Lecture Notes in Artificial Intelligence Springer-Verlag.CrossRefGoogle Scholar
Michalski, RS and Stepp, RE, 1983. “Learning from observation: conceptual clustering”. In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: an artificial intelligence approach, vol 1, Tioga Publishing Co.CrossRefGoogle Scholar
Mitchell, TM, 1977. “Version spaces: a candidate elimination approach to rule learning”. In: Proceedings of the 5th International Joint Conference on Artificial IntelligenceMorgan Kaufmann.Google Scholar
Mitchell, TM, 1982. “Generalization as search”, Artificial Intelligence 18 203–226.CrossRefGoogle Scholar
Mitchell, TM, Utgoff, PE and Banerji, R, 1983. “Learning by experimentation: acquiring and refining problem-solving heuristics”. In: Michalski, RS, Carbonnel, JG and Mitchell, TM, editors, Machine Learning: an artificial intelligence approach pp 163–190, Tioga Publishing Co.Google Scholar
Muggleton, S and Buntine, W, 1988. “Machine invention of first order predicates by inverting resolution”. In: Proceedings of the 5th International Conference on Machine Learning pp 339–351, Morgan Kaufmann.CrossRefGoogle Scholar
Nilsson, NJ, 1980. Principles of Artificial Intelligence Tioga Publishing Co.Google Scholar
Quinlan, JR, 1986. “Induction of decision trees” Machine Learning 1 81–106.CrossRefGoogle Scholar
Quinlan, JR, 1987. “Generating production rules from decision trees”. In: Proceedings of the 10th International Joint Conference on Artificial Intelligence pp 304–307, Morgan Kaufmann.Google Scholar
Quinlan, JR, 1990, “Learning logical definition from relations” Machine Learning 5 239–266.CrossRefGoogle Scholar
Rendell, L, 1986. “A general framework for induction and a study of selective induction” Machine Learning 1 177–226.CrossRefGoogle Scholar
Sablon, G, Ade, H, De Raedt, L and Bruynooghe, M, 1992. “Some thoughts on inverse resolution”. In: Muggleton, S, editor, Inductive Logic Programming Academic Press.Google Scholar
Sammut, C and Banerji, R, 1986. “Learning concepts by asking questions”, In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: an artificial intelligence approach vol. 2, pp 167–192, Morgan Kaufmann.Google Scholar
Shapiro, EY, 1981. “An algorithm that infers theories from facts”. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp 446–452, Morgan Kaufmann.Google Scholar
Shapiro, EY, 1983. Algorithmic Program Debugging MIT Press.Google Scholar
Subramanian, D and Feigenbaum, J, 1988. “Factorization in experiment generation”. In: Proceedings of the 5th American Conference On Artificial Intelligence, pp 518–522, Morgan Kaufmann.Google Scholar
Tcheng, D, Lamber, B, SC, Lu and Rendell, L, 1989. “Building robust learning Systems by combining induction and optimization”, In: Proceedings of the 11th International Joint Conference on Artificial Intelligence pp 806–812, Morgan Kaufmann.Google Scholar
Valiant, L, 1984. “A theory of the learnable” Communications of the ACM 27 1134–1142.CrossRefGoogle Scholar
Vere, SA, 1975. “Induction of concepts in the predicate calculus”, In Proceedings of the 4th International Joint Conference on Artificial Intelligence pp 282–287, Morgan Kaufmann.Google Scholar
Wrobel, S, 1988. “Automatic representation adjustment in an observational discovery System”. In: Sleeman, D, editor, Proceedings of the 3rd European Working Session on Learning Pitman.Google Scholar
Wrobel, S, 1989. “Demand driven concept-formation”. In: Morik, K, editor, Knowledge Representation and Organization in Machine Learning, vol. 347 of Lecture Notes in Artificial Intelligence, Springer-Verlag.CrossRefGoogle Scholar