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Priors for Neural Networks

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Classification, Clustering, and Data Mining Applications

Abstract

Neural networks are commonly used for classification and regression. The Bayesian approach may be employed, but choosing a prior for the parameters presents challenges. This paper reviews several priors in the literature and introduces Jeffreys priors for neural network models. The effect on the posterior is demonstrated through an example.

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References

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Author information

Authors and Affiliations

  1. University of California at Santa Cruz, USA

    Herbert K. H. Lee

Authors
  1. Herbert K. H. Lee
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Editor information

Editors and Affiliations

  1. Leanna House Institute of Statistics and Decision Sciences, Duke University, 27708, Durham, NC, USA

    David Banks

  2. Department of Mathematics, Illinois Institute of Technology, 10 West 32nd Street, 60616-3793, Chicago, IL, USA

    Frederick R. McMorris

  3. Faculty of Management, Rutgers University, 180 University Avenue, 07102-1895, Newark, NJ, USA

    Phipps Arabie

  4. Institute of Decision Theory, University of Karlsruhe, Kaiserstr. 12, 76128, Karlsruhe, Germany

    Wolfgang Gaul

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© 2004 Springer-Verlag Berlin Heidelberg

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Lee, H.K.H. (2004). Priors for Neural Networks. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_14

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  • DOI: https://doi.org/10.1007/978-3-642-17103-1_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22014-5

  • Online ISBN: 978-3-642-17103-1

  • eBook Packages: Springer Book Archive

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