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Bayesian Learning of Neural Networks Adapted to Changes of Prior Probabilities

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

We treat Bayesian neural networks adapted to changes in the ratio of prior probabilities of the categries. If an ordinary Bayesian neural network is equipped with m–1 additional input units, it can learn simultaneously m distinct discriminant functions which correspond to the m different ratios of the prior probabilities.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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Author information

Authors and Affiliations

  1. Department of Information and Policy Studies, Aichi-Gakuin University, Nisshin, Aichi-ken, 470-0195, Japan

    Yoshifusa Ito & Hiroyuki Izumi

  2. Department of Statistics, University of Kentucky, 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. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

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

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Ito, Y., Srinivasan, C., Izumi, H. (2005). Bayesian Learning of Neural Networks Adapted to Changes of Prior Probabilities. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_40

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  • DOI: https://doi.org/10.1007/11550907_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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