Minimal distance neural methods

Abstract

A general framework for minimal distance methods is presented. Radial basis functions (RBFs) and multilayer perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on real-world datasets obtaining very encouraging results.