Improving the performance of probabilistic neural networks

Abstract

A methodology for selection of appropriate widths or covariance matrices of the Gaussian functions in implementations of PNN (probabilistic neural network) classifiers is presented. The Gram-Schmidt orthogonalization process is employed to find these matrices. It has been shown that the proposed technique improves the generalization ability of the PNN classifiers over the standard approach. The result can be applied to other Gaussian-based classifiers such as the radial basis functions.<>