On different model selection criteria in a forward and backward regression hybrid network

Shimon Cohen, Nathan Intrator

Research output: Contribution to journalArticlepeer-review

Abstract

An assessment of the performance of a hybrid network with different model selection criteria is considered. These criteria are used in an automatic model selection algorithm for constructing a hybrid network of radial and perceptron hidden units for regression. A forward step builds the full hybrid network; a model selection criterion is used for controlling the network-size and another criterion is used for choosing the appropriate hidden unit for different regions of input space. This is followed by a conservative pruning step using Likelihood Ratio Test, Bayesian or Minimum Description Length, which leads to robust estimators with low variance. The result is a small architecture that performs well on difficult, realistic, benchmark data-sets of high dimensionality and small training size. Best results are obtained by using the Bayesian approach for the model selection.

Original languageEnglish
Pages (from-to)847-865
Number of pages19
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume18
Issue number5
DOIs
StatePublished - Aug 2004

Keywords

  • Clustering
  • Hybrid network architecture
  • Projection units
  • RBF units
  • Regularization
  • SMLP

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