Abstract
Nested Generalized Exemplar (NGE) theory [1] is an incremental form of inductive learning from examples. This paper presents FNGE, a learning system based on a fuzzy version of the NGE theory, describes its main modules and discusses some empirical results from its use in public domains
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References
Salzberg, S.L., “A Nearest Hyperrectangle Learning Method”, Machine Learning 6, 251–276, 1991
Nicoletti M.C.; Santos, F.O., “Learning Fuzzy Exemplars through a Fuzzified Nested Generalized Exemplar Theory”, Proceedings of EFDAN’96, Germany, 140–145, 1996
Merz, C.J. and Murphy, P.M., “UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/ mlearn/MLRepository.html]. Irvine, CA, 1998
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© 1998 Springer-Verlag Berlin Heidelberg
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do Nicoletti, M.C., Santos, F.O. (1998). A Constructive Fuzzy NGE Learning System. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_54
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DOI: https://doi.org/10.1007/3-540-49292-5_54
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