Abstract
An exemplar-based model with foundations in Bayesian networks is described. The proposed model utilises two Bayesian networks: one for indexing of categories, and another for identifying exemplars within categories. Learning is incrementally conducted each time a new case is classified. The representation structure dynamically changes each time a new case is classified and a coverage function is used as a basis for selecting suitable exemplars. Finally, a simple example is given to illustrate the concepts in the model.
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References
D.W. Aha. Case-based learning algorithms. In Proc. of the DARPA Case-Based Reasoning Workshop, pages 147–158, Washington, D.C.: Morgan Kaufmann, 1991.
D.W. Aha and L.W. Chang. Cooperative bayesian and case-based reasoning for solving multiagent planning tasks. Technical Report AIC-96-005, Navy Center for Applied Research in AI Naval Research Laboratory, Washington, DC, U.S.A., 1996.
R. Bareiss. Exemplar-based knowledge acquisition. A unified approach to concept representation, classification, and learning. Academic Press Inc., Harcourt Brace Jovanovich Publishers, San Diego, Calif., U.S.A., 1989.
H.W. Beck, T. Anwar, and S.B. Navathe. A conceptual clustering algorithm for database schema design. IEEE Transaction on Knowledge and Data Engineering, 6(3):396–411, 1994.
Y. Biberman. The role of prototypicality in exemplar-based learning. In Nada Lavrač and Stefan Wrobel, editors, Proc. Machine Learning: EGML-95, 8th European Conference on Machine Learning, pages 77–91, Heraclion, Crete, Greece, 1995.
J.S. Breese and D. Heckerman. Decision-theoretic case-based reasoning. In Proc. of the Fifth International Workshop on Artificial Intelligence and Statistics, pages 56–63, Ft. Lauderdale, U.S.A., 1995.
L. Chang and P. Harrison. A case-based reasoning testbed for experiments in adaptive memory retrieval and indexing. In D.H. Aha and A. Ram, editors, Proc. of the AAAI fall Symposium on Adaptation of Knowledge for Reuse, Menlo Park: AAAI Press, 1995.
T. Dean, J. Allen, and Y. Aloimonos. Artificial Intelligence theory and practice. The Benjamin/Cummings Publishing Company, Inc., Redwood City, Calif., U.S.A., 1995.
D.L. Medin and M.M. Schaffer. Contex theory of clasification learning. Psychological Review, 85:207–238, 1978.
S. Minton, J.G. Carbonell, C.A. Knoblock, D.R. Kuokka, O. Etzioni, and Y. Gil. Explanation-based learning: a problem solving perspective. In Jaime Carbonell, editor, Machine Learning: Paradigms and Methods, pages 63–118. MIT/Elsevier Science, Cambridge, Massachusetts, U.S.A., 1990.
P. Myllymäki and H. Tirri. Massively parallel case-based reasoning with probabilistic similarity metrics. In Klauss-Dieter Althoff Stefan Wess and Michael M. Ritcher, editors, Topics in Case-Based Reasoning, pages 144–154. Volume 837, Lecture Notes in Artificial Intelligence. Springer Verlag, 1994.
J. Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, Palo Alto, Calif., U.S.A., 1988.
B.W. Porter, R. Bareiss, and R.C. Holte. Concept learning and heuristic classification in weak-theory domains. Artificial Intelligence, University of Texas, Austin Texas, U.S.A., (45):229–263, 1990.
E. Rosch and C.B. Mervis. Family resemblance studies in the internal structure of categories. Cognitive Psychology, (7):573–605, 1975.
E. Smith and D. Medin. Categories and concepts. Cambride: Harvard University Press, U.S.A., 1981.
H. Tirri, P. Kontkanen, and P. Myllymäki. A bayesian framework for case-based reasoning. In Proc. of the 3rd European Workshop on Case-based Reasoning, Lausanne, Switzerland, 1996.
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Rodriguez, A.F., Vadera, S., Sucar, L.E. (1997). A probabilistic model for case-based reasoning. In: Leake, D.B., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 1997. Lecture Notes in Computer Science, vol 1266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63233-6_530
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DOI: https://doi.org/10.1007/3-540-63233-6_530
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