Case-based and symbolic classification
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Contrary to symbolic learning approaches, that represent a learned concept explicitly, case-based approaches describe concepts implicitly by a pair (CB,sim), i.e. by a measure of similarity sim and a set CB of cases. This poses the question if there are any differences concerning the learning power of the two approaches. In this article we will study the relationship between the case base, the measure of similarity, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the hypothesis of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.
- David W. Aha. Case-Based Learning Algorithms. In Ray Bareiss, editor, Proceedings CBR Workshop 1991, pages 147–158. Morgan Kaufmann Publishers, 1991.
- David W. Aha, Dennis Kibler, and Marc K. Albert. Instance-Based Learning Algorithms. Machine Learning, 6:37–66, 1991.
- R. Bareiss, B. W. Porter, and C. C. Wier. Protos: An Exemplar-Based Learning Apprentice. In Kodratoff and Michalski , pages 12–23.
- S. Cost and Steven Salzberg. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10(1):56–78, 1993.
- Belur Dasarathy. Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, 1990.
- Christoph Globig and Stefan Wess. Symbolic Learning and Nearest-Neighbor Classification. In H.-H. Bock, W. Lenski, and M.M. Richter, editors, Information Systems and Data-Analysis, pages 17–27. Springer Verlag, 1994.
- Robert S. Holte. Commentary on: Protos an exemplar-based learning apprentice. In Kodtratoff and Michalski , pages 128–139.
- Klaus P. Jantke. Case-Based Learning in Inductive Inference. In Proceedings of the 5th ACM Workshop on Computational Learning Theory (COLT-92), pages 218–223. ACM Press, 1992.
- Yves Kodratoff and Ryszard Michalski, editors. Machine Learning: An Artificial Inteligence Approach, volume III. Morgan Kaufmann, 1990.
- Janet L. Kolodner. Retrieval and Organizational Strategies in Conceptual Memory. Lawrence Erlbaum, Hillsdale, New Jersey, 1984.
- R. Michalski, J. G. Carbonell, and T. Mitchell, editors. Machine Learning: An Artificial Intelligence Approach, volume 1. Tioga, Palo Alto, California, 1983.
- Ryszard S. Michalski. Concept Meaning, Matching and Cohesiveness. In Stella Vosniadou and Andrew Ortony, editors, Similarity and Analogical Reasoning, chapter 4, pages 122–145. Cambridge University Press, Cambridge, 1989.
- T.M. Mitchell. Generalization as search. Artificial Intelligence, 18(2):203–226, 1982.
- L. Rendell. A general framework for induction and a study of selective induction. Machine Learning, (1):177–226, 1986.
- Michael M. Richter. Classification and Learning of Similarity Measures. In Proc. der 16. Jahrestagung der Gesellschaft für Klassifikation e. V. Springer Verlag, 1992.
- Michael M. Richter and Stefan Wess. Similarity, Uncertainty and Case-Based Reasoning in Patdex. In Robert S. Boyer, editor, Automated Reasoning, Essays in Honor of Woody Bledsoe, pages 249–265. Kluwer Academic Publishing, 1991.
- Craig Stanfill and David Waltz. Toward Memory-Based Reasoning. Com. of the ACM, 29(12):1213–1229, 1986.
- Case-based and symbolic classification
- Book Title
- Topics in Case-Based Reasoning
- Book Subtitle
- First European Workshop, EWCBR-93 Kaiserslautern, Germany, November 1–5, 1993 Selected Papers
- pp 77-91
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- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series Subtitle
- Lecture Notes in Artificial Intelligence
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- Springer Berlin Heidelberg
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