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Analytical Decision Boundary Feature Extraction for Neural Networks with Multiple Hidden Layers

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Computer Analysis of Images and Patterns (CAIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2756))

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Abstract

A feature extraction method based on decision boundaries has been proposed for neural networks. The method is based on the fact that normal vectors to the decision boundary provide the information necessary for discriminating between classes. However, it is observed that the previous implementation of numerical approximation of the gradient has resulted in some performance loss and a long processing time. In this paper, we propose a new method to calculate normal vectors analytically for neural networks with multiple hidden layers. Experiments showed noticeable improvements in performance and speed.

This work was supported in part by Biometrics Engineering Research Center (KOSEF).

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, BERC, Yonsei University, 134 Shinchon-Dong, Seodaemoon-Gu, Seoul, Korea

    Jinwook Go & Chulhee Lee

Authors
  1. Jinwook Go
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  2. Chulhee Lee
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Editor information

Editors and Affiliations

  1. Institute of Mathematics and Computing Science, University of Groningen, P.O.Box 800, 9700 AV, Groningen, The Netherlands

    Nicolai Petkov

  2. Institute of Mathematics and Computing Science, University of Groningen, Blauwborgje 3, 9747 AC, Groningen, The Netherlands

    Michel A. Westenberg

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© 2003 Springer-Verlag Berlin Heidelberg

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Go, J., Lee, C. (2003). Analytical Decision Boundary Feature Extraction for Neural Networks with Multiple Hidden Layers. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_71

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  • DOI: https://doi.org/10.1007/978-3-540-45179-2_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40730-0

  • Online ISBN: 978-3-540-45179-2

  • eBook Packages: Springer Book Archive

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