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Neural Network Modeling by Subsampling

  • Conference paper

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

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.

Keywords

  • Neural Network
  • Model Selection Strategy
  • Proposed Test Procedure
  • Universal Approximation Property
  • Hide Layer Size

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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)