Automatic Model Selection in a Hybrid Perceptron/Radial Network

  • Shimon Cohen
  • Nathan Intrator
Conference paper

DOI: 10.1007/3-540-48219-9_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)
Cite this paper as:
Cohen S., Intrator N. (2001) Automatic Model Selection in a Hybrid Perceptron/Radial Network. In: Kittler J., Roli F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg

Abstract

We introduce an algorithm for incrementaly constructing a hybrid network fo radial and perceptron hidden units. The algorithm determins if a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determins the number of hidden units. This results in a final architecture which is often much smaller than an RBF network or a MLP. A benchmark on four classification problems and three regression problems is given. The most striking performance improvement is achieved on the vowel data set [4].

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Shimon Cohen
    • 1
  • Nathan Intrator
    • 1
  1. 1.Computer Science DepartmentTel-Aviv UniversityIsrael

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