Forward and Backward Selection in Regression Hybrid Network

  • Shimon Cohen
  • Nathan Intrator
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2364)

Abstract

We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm determines whether a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and prunes unnecessary weights. This results in a final architecture which is often much smaller than a RBF network or a MLP. Results for various data sizes on the Pumadyn data indicate that the resulting architecture competes and often outperform best known results for this data set.

Keywords

Hybrid Network Architecture SMLP Clustering Regularization Nested Models Model Selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Shimon Cohen
    • 1
  • Nathan Intrator
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityIsrael

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