Abstract
In this paper, we propose a learning method of minimal casebase to represent taxonomic relation in a tree-structured concept hierarchy. We firstly propose case-based taxonomic reasoning and show an upper bound of necessary positive cases and negative cases to represent a relation. Then, we give an learning method of a minimal casebase with sampling and membership queries. We analyze this learning method by sample complexity and query complexity in the framework of PAC learning.
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References
Ashley, K. D.: Modeling Legal Argument: Reasoning with Cases and Hypotheticals MIT press (1990) 181
Ashley, K. D., and Aleven, V.: A Logical Representation for Relevance Criteria. S. Wess, K-D. Althoff and M. Richter (eds.) Topics in Case-Based Reasoning, LNAI 837 (1994) 338–352 181
Bareis, R.: PROTOS; a Unified Approach to Concept Representation, Classification and Learning. Ph.D. Dissertation, University of Texas at Austin, Dep. of Computer Sciences (1988) 180
Bshouty, N. H.: Exact Learning Boolean Functions via the Monotone Theory. Information and Computation 123 (1995) 146–153 180
Matuschek, D., and Jantke, K. P.: Axiomatic Characterizations of Structural Similarity for Case-Based Reasoning. Proc. of Florida AI Research Symposium (FLAIRS-97) (1997) 432–436 181
Khardon, R., and Roth, D.: Reasoning with Models. Artificial Intelligence 87 (1996) 187–213 180
Osborne, H. R., and Bridge, D. G.: A Case Base Similarity Framework. Advances in Case-Based Reasoning, LNAI 1168 (1996) 309–323 181
Satoh, K.: Analysis of Case-Based Representability of Boolean Functions by Monotone Theory. Proceedings of ALT’98 (1998) 179–190 180
Satoh, K., and Ryuich Nakagawa: Discovering Critical Cases in Case-Based Reasoning (Extended Abstract). Online Proceedings of 6th Symposium on AI and Math, http://rutcor.rutgers.edu/amai/AcceptedCont.htm (2000) 180, 187
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Satoh, K. (2000). Learning Taxonomic Relation by Case-based Reasoning. In: Arimura, H., Jain, S., Sharma, A. (eds) Algorithmic Learning Theory. ALT 2000. Lecture Notes in Computer Science(), vol 1968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40992-0_14
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DOI: https://doi.org/10.1007/3-540-40992-0_14
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