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Neural Network Modeling by Subsampling

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

The aim of the paper is to develop hypothesis testing procedures both for variable selection and model adequacy to facilitate a model selection strategy for neural networks. The approach, based on statical inference tools, uses the subsampling to overcome the analytical and probabilistic difficulties related to the estimation of the sampling distribution of the test statistics involved. Some illustrative examples are also discussed.

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Author information

Authors and Affiliations

  1. Dept. of Economics and Statistics, University of Salerno, Via Ponte don Melillo, 84084, Fisciano, SA, Italy

    Michele La Rocca & Cira Perna

Authors
  1. Michele La Rocca
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  2. Cira Perna
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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La Rocca, M., Perna, C. (2005). Neural Network Modeling by Subsampling. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_25

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  • DOI: https://doi.org/10.1007/11494669_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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