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A k-Winner-Takes-All Classifier for Structured Data

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KI 2003: Advances in Artificial Intelligence (KI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2821))

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Abstract

We propose a k-winner-takes-all (KWTA) classifier for structures represented by graphs. The KWTA classifier is a neural network implementation of the k-nearest neighbor (KNN) rule. The commonly used comparator for identifying the k nearest neighbors of a given input structure is replaced by an inhibitory winner-takes-all network for k-maximum selection. Due to the principle elimination of competition the KWTA classifier circumvents the problem of determining computational intensive structural similarities between a given input structure and several model structures. In experiments on handwritten digits we compare the performance of the self-organizing KWTA classifier with the canonical KNN classifier, which uses a supervising comparator.

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Author information

Authors and Affiliations

  1. Dept. of Electrical Engineering and Computer Science, Technical University Berlin, Germany

    Brijnesh J. Jain & Fritz Wysotzki

Authors
  1. Brijnesh J. Jain
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  2. Fritz Wysotzki
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Editor information

Editors and Affiliations

  1. HITeC e. V. Universität Hamburg Fachbereich Informatik, Vogt-Kölln-Str. 30, 22527, Hamburg, Germany

    Andreas Günter

  2. Otto-von-Guericke-University of Magdeburg,  

    Rudolf Kruse

  3. Cognitive Systems Laboratory, Department Informatik, Universität Hamburg, 22527, Hamburg, Germany

    Bernd Neumann

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© 2003 Springer-Verlag Berlin Heidelberg

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Jain, B.J., Wysotzki, F. (2003). A k-Winner-Takes-All Classifier for Structured Data. In: Günter, A., Kruse, R., Neumann, B. (eds) KI 2003: Advances in Artificial Intelligence. KI 2003. Lecture Notes in Computer Science(), vol 2821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39451-8_25

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  • DOI: https://doi.org/10.1007/978-3-540-39451-8_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20059-8

  • Online ISBN: 978-3-540-39451-8

  • eBook Packages: Springer Book Archive

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