Chapter

Multiple Classifier Systems

Volume 2364 of the series Lecture Notes in Computer Science pp 98-107

Date:

Forward and Backward Selection in Regression Hybrid Network

  • Shimon CohenAffiliated withSchool of Computer Science, Tel Aviv University
  • , Nathan IntratorAffiliated withSchool of Computer Science, Tel Aviv University

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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