Abstract
While symbolic learning approaches encode the knowledge provided by the presentation of the cases explicitly into a symbolic representation of the concept, e.g. formulas, rules, or decision trees, case-based approaches describe learned concepts implicitly by a pair (CB, d), i.e. by a set CB of cases and a distance measure d. Given the same information, symbolic as well as the case-based approach compute a classification when a new case is presented. This poses the question if there are any differences concerning the learning power of the two approaches. In this work we will study the relationship between the case base, the measure of distance, 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 conjecture of the equivalence of the learning power of symbolic and case-based methods and show the interdependency between the measure used by a case-based algorithm and the target concept.
The presented work was partly supported by the Deutsche Forschungsgemeinschaft, project IND-CBL.
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References
Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
Aha, D. W. (1991). Case-Based Learning Algorithms. In Bareiss, R. (Ed.), Proceedings: Case-Based Reasoning Workshop, pp. 147–158. Morgan Kaufmann Publishers.
Angluin, D. (1980). Inductive inference of formal languages from positive data. Information and Control, 45, 117–135.
Angluin, D., & Smith, C. H. (1983). Inductive Inference: Theory and Methods. Computing Surveys, 15(3), 237–269.
Dasarathy, B. (1990). Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Computer Society Press.
Globig, C., & Lange, S. (1994). On case-based representability and learnability of languages. In Arikawa, S., & Jantke, K. (Eds.), Algorithmic Learning Theory, Vol. 872 of LNAI, pp. 106–121. Springer-Verlag.
Globig, C. (1993). Symbolisches und Fallbasiertes Lernen. Masters Thesis, University of Kaiserslautern.
Globig, C., & Wess, S. (1994). Symbolic Learning and Nearest-Neighbor Classification. In Bock, P., Lenski, W., & Richter, M. M. (Eds.), Information Systems and Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 17–27. Springer Verlag.
Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.
Holte, R. S. (1990). Commentary on: Protos an exemplar-based learning apprentice. In Kodtratoff, Y., & Michalski, R. (Eds.), Machine Learning: An Artificial Intelligence Approach, Vol. III, pp. 128–139. Morgan Kaufmann.
Jantke, K. P. (1992). Case-Based Learning in Inductive Inference. In Proceedings of the 5th ACM Workshop on Computational Learning Theory (COLT'92), pp. 218–223. ACM-Press.
Jantke, K. P., & Lange, S. (1989). Algorithmisches lernen. In Grabowski, J., Jantke, K. P., & Thiele, H. (Eds.), Grundlagen der Künstlichen Intelligenz, pp. 246–277. Akademie-Verlag, Berlin.
Jantke, K., & Lange, S. (1993). Case-based representation and learning of pattern languages. In Proceedings of the 4th International Workshop on Algorithmic learning Theory (ALT'93), Vol. 744 of LNAI, pp. 87–100. Springer-Verlag.
Kolodner, J. L. (1993). Case-Based Reasoning. Morgan Kaufmann.
Michalski, R., Carbonell, J. G., & Mitchell, T. (Eds.). (1983). Machine Learning: An Artificial Intelligence Approach, Vol. 1. Tioga, Palo Alto, California.
Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18(2), 203–226.
Rendell, L. (1986). A General Framework for Induction and a Study of Selective Induction. Machine Learning, 1, 177–226.
Richter, M. M. (1992). Classification and Learning of Similarity Measures. In Proc. der 16. Jahrestagung der Gesellschaft für Klassifikation e.V. Springer Verlag.
Wess, S., & Globig, C. (1994). Case-based and symbolic classification — a case study. In Wess, S., Althoff, K.-D., & Richter, M. (Eds.), Topics in Case-Based Reasoning, Vol. 837 of Lecture Notes in Artificial Intelligence, pp. 65–76. Springer-Verlag.
Wess, S. (1993). Patdex — Inkrementelle und wissensbasierte Verbesserung von Ähnlichkeitsurteilen in der fallbasierten Diagnostik. In Tagungsband 2. deutsche Expertensystemtagung XPS-93, pp. 42–55 Hamburg. Springer Verlag.
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Globig, C., Wess, S. (1995). Learning in case-based classification algorithms. In: Jantke, K.P., Lange, S. (eds) Algorithmic Learning for Knowledge-Based Systems. Lecture Notes in Computer Science, vol 961. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60217-8_16
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DOI: https://doi.org/10.1007/3-540-60217-8_16
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