- Published 2004 in IJPRAI

An assessment of the performance 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.

@article{Cohen2004OnDM,
title={On Different Model Selection Criteria In A Forward And Backward Regression Hybrid Network},
author={Shimon Cohen and Nathan Intrator},
journal={IJPRAI},
year={2004},
volume={18},
pages={847-865}
}