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Discriminant Analysis by a Neural Network with Mahalanobis Distance

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Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

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Abstract

We propose a neural network which can approximate Mahalanobis discriminant functions after being trained. It can be realized if a Bayesian neural network is equipped with two additional subnetworks. The training is performed sequentially and, hence, the past teacher signals need not be memorized. In this paper, we treat the two-category normal-distribution case. The results of simple simulations are included.

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Author information

Authors and Affiliations

  1. Department of Policy Science, Aichi-Gakuin University, Nisshin, Aichi-ken, 470-0195, Japan

    Yoshifusa Ito & Hiroyuki Izumi

  2. Department of Statistics, University of Kentucky, Patterson Office Tower, Lexington, Kentucky, 40506, USA

    Cidambi Srinivasan

Authors
  1. Yoshifusa Ito
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  2. Cidambi Srinivasan
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  3. Hiroyuki Izumi
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Ito, Y., Srinivasan, C., Izumi, H. (2006). Discriminant Analysis by a Neural Network with Mahalanobis Distance. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_36

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  • DOI: https://doi.org/10.1007/11840930_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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