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Learning membership functions

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Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 747))

Abstract

An efficient method for learning membership functions for fuzzy predicates is presented. Positive and negative examples of one class are given together with a system of classification rules. The learned membership functions can be used for the fuzzy predicates occurring in the given rules to classify further examples. We show that the obtained classification is approximately correct with high probability. This justifies the obtained fuzzy sets within one particular classification problem, instead of relying on a subjective meaning of fuzzy predicates as normally done by a domain expert.

Consorzio per la Ricerca sulla Microelettronica nel Mezzogiorno, Universitá di Catania and SGS-Thomson

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Catania, Catania, Italy

    Francesco Bergadano

  2. Fuzzy Logic R & D Group, Co.Ri.M.Me. Research Center, Catania, Italy

    Vincenzo Cutello

Authors
  1. Francesco Bergadano
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  2. Vincenzo Cutello
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Editor information

Michael Clarke Rudolf Kruse Serafín Moral

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© 1993 Springer-Verlag Berlin Heidelberg

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Bergadano, F., Cutello, V. (1993). Learning membership functions. In: Clarke, M., Kruse, R., Moral, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1993. Lecture Notes in Computer Science, vol 747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028178

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  • DOI: https://doi.org/10.1007/BFb0028178

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57395-1

  • Online ISBN: 978-3-540-48130-0

  • eBook Packages: Springer Book Archive

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