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Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

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Abstract

We derive optimal radial kernel in the radial basis function network applied in nonlinear function learning and classification.

This research was supported by the Alexander von Humboldt Foundation and Natural Science and Engineering Research Council of Canada

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Author information

Authors and Affiliations

  1. Department of Computer Science, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, Quebec, Canada, H3G 1M8

    Adam Krzyżak

Authors
  1. Adam Krzyżak
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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© 2001 Springer-Verlag Berlin Heidelberg

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Krzyżak, A. (2001). Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_18

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  • DOI: https://doi.org/10.1007/3-540-44596-X_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

  • eBook Packages: Springer Book Archive

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