Integrated feature architecture selection.

In this paper, we present an integrated approach to feature and architecture selection for single hidden layer-feedforward neural networks trained via backpropagation. In our approach, we adopt a statistical model building perspective in which we analyze neural networks within a nonlinear regression framework. The algorithm presented in this paper employs a likelihood-ratio test statistic as a model selection criterion. This criterion is used in a sequential procedure aimed at selecting the best neural network given an initial architecture as determined by heuristic rules. Application results for an object recognition problem demonstrate the selection algorithm's effectiveness in identifying reduced neural networks with equivalent prediction accuracy.