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An Iterative Fuzzy Prototype Induction Algorithm

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Computational Methods in Neural Modeling (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2686))

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Abstract

This paper is concerned with the automated induction of prototypes to represent a database in a way that combines transparency and accuracy. A clustering algorithm will be described which learns fuzzy prototypes from a set of data. The potential of the resulting method will be illustrated by its application to classification problems and comparing its performance with that of previous approaches in the literature.

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Author information

Authors and Affiliations

  1. Dept. de Ciencias Experimentales e Ingeniería, Universidad Rey Juan Carlos, Avenida Tulipán s/n, 28339, Móstoles (Madrid), Spain

    Inés González Rodríguez

  2. A.I. Group, Dept. of Engineering Mathematics, University of Bristol, University Walk, BS8 1TR, Bristol, UK

    Jonathan Lawry & Jim F. Baldwin

Authors
  1. Inés González Rodríguez
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  2. Jonathan Lawry
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  3. Jim F. Baldwin
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Rodríguez, I.G., Lawry, J., Baldwin, J.F. (2003). An Iterative Fuzzy Prototype Induction Algorithm. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_37

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  • DOI: https://doi.org/10.1007/3-540-44868-3_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

  • eBook Packages: Springer Book Archive

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