Abstract
In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This may be necessary if the initial representation is inadequate or inappropriate. However, the distinction between constructive and non-constructive methods appears to be highly ambiguous. Several conventional definitions of the process of constructive induction appear to include all conceivable learning processes. In this paper I argue that the process of constructive learning should be identified with that of relational learning (i.e., I suggest that what constructive learners really learn is relationships) and I describe some of the possible benefits that might be obtained as a result of adopting this definition.
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References
Aronis, J. & Provost, F. (1994). Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. Proceedings of ML-COLT'94.
Baum, E. & Haussler, D. (1990). What Size Net Gives Valid Generalization? In Shavlik, J.W. & Dietterich, T. G. (eds.) Readings in Machine Learning, 258-262. San Mateo, California: Morgan Kaufmann.
Blumer, A., Ehrenfeucht, A., Haussler, D. & Warmuth, M. (1987). Occam's Razor. Information Processing Letters 24: 377-380.
Clark, P. & Boswell, R. (1991). Rule Induction with CN2: Some Recent Improvements. In Kodratoff, Y. (ed.) Proceedings of the Fifth European Working Session on Learning, No. 482 of Lecture Notes in Artificial Intelligence, 151-163. Springer-Verlag.
Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning 3: 261-283.
Dietterich, T. & Michalski, R. (1983). A Comparative Review of Selected Methods for Learning from Examples. In Michalski, R., Carbonell, J. & Mitchell, J. (eds.) Machine Learning: An Artificial Intelligence Approach. Palo Alto: Tioga.
Fawcett, T. & Utgoff, P. (1991). A Hybrid Method for Feature Generation. Proceedings of the Eighth International Workshop on Machine Learning, 137-141. Evanston, IL.
Gold, E. (1967). Language Identification in the Limit. Information and Control 10: 447-474.
Haussler, D. (1986). Quantifying the Inductive Bias in Concept Learning. UCSC-CRL-86-25, University of California at Santa Cruz.
Haussler, D. (1987). Bias, Version Spaces and Valiant's Learning Framework. Proceedings of the Fourth International Workshop on Machine Learning, 324-336. University of California, Irvine (June 22–25).
Haussler, D. (1988). Quantifying Inductive Bias: AI Learning and Valiant's Learning Framework. Artificial Intelligence 36: 177-221.
Haussler, D., Kearns, M. & Schapire, R. (1992). Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. UCSC-CRL-91-44, University of California at Santa Cruz.
Japkowicz, N. & Hirsh, H. (1994). Towards a Bootstrapping Approach to Constructive Induction. Proceedings of ML-COLT'94.
Kearns, M. (1990). The Computational Complexity of Machine Learning. The MIT Press.
Kramer, S. (1994). CN2-MCI: A Two-Step Method for Constructive Induction. Proceedings of ML-COLT'94.
Matheus, C. (1990). Adding Domain Knowledge to SBL Through Feature Construction. Proceedings of the Eighth National Conference on Artificial Intelligence, 803-808. Boston, MA: MIT Press.
Michalski, R. (1983). A Theory and Methodology of Inductive Learning. In Michalski R., Carbonell, J. & Mitchell, T. (eds.) Machine Learning: An Artificial Intelligence Approach. Palo Alto: Tioga.
Michalski, R., Carbonell, J. & Mitchell, T. (eds.) (1983). Machine Learning: An Artificial Intelligence Approach. Palo Alto: Tioga.
Michalski, R., Carbonell, J. & Mitchell, T. (eds.) (1986). Machine Learning: An Artificial Intelligence Approach, Vol II. Los Altos: Morgan Kaufmann.
Mitchell, T. (1997). Machine Learning. McGraw-Hill.
Murphy, P. & Pazzani, M. (1991). ID2-of-3: Constructive Induction of M-of-N Concepts for Discriminators in Decision Trees. Proceedings of the Eighth International Workshop on Machine Learning (ML91). San Mateo, CA: Morgan Kaufmann.
Oliveira, A. & Sangiovanni-Vincentelli, A. (1992). Constructive Induction Using a Non-Greedy Strategy for Feature Selection. In Sleeman, D. & Edwards, P. (eds.) Proceedings of the Ninth International Workshop on Machine Learning (ML92), 355-360. San Mateo, California: Morgan Kaufmann Publishers.
Pagallo, G. (1989). Learning DNF by Decision Trees. Proceedings of the Eleventh Joint Conference on Artificial Intelligence, 639-644. Morgan Kaufmann.
Pfahringer, B. (1994). Cipf 2.0: A Robust Constructive Induction System. Proceedings of ML-COLT'94.
Quinlan, J. (1993). C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann.
Rumelhart, D., Hinton, G. & Williams, R. (1986). Learning Representations by Back-Propagating Errors. Nature 323: 533-536.
Sazonov, V. & Wnek, J. (1994). A Hypothesis-Driven Constructive Induction Approach to Expanding Neural Networks. Proceedings of ML-COLT'94.
Seshu, R. (1989). Solving the Parity Problem. University of Illinois at Urbana-Champaign, Inductive Learning Group.
Spackman, K. (1988). Learning Categorical Decision Criteria in Biochemical Domains. Proceedings of the Fifth International Conference on Machine Learning, 36-46. San Mateo, CA: Morgan Kaufmann.
Stone, J. & Thornton, C. (1995). Can Artificial Neural Networks Discover Useful Regularities? Proceedings of ICANN-95. Cambridge.
Valiant, L. (1984). A Theory of the Learnable. Communications of the ACM 27: 1134-1142.
Valiant, L. (1995). Learning Disjunctions of Conjunctions. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 560-566. Los Altos: Morgan Kaufmann.
Vapnik, V. & Chervonenkis, A. (1971). On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Theor. Probab. Appl. 16(2): 264-280.
Wnek, J. & Michalski, R. (1994). Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments. Machine Learning 14, 139. Boston: Kluwer Academic Publishers.
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Thornton, C. What do Constructive Learners Really Learn?. Artificial Intelligence Review 13, 249–257 (1999). https://doi.org/10.1023/A:1006577209231
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DOI: https://doi.org/10.1023/A:1006577209231