Non Parametric Classification Using Learning Vector Quantization

Abstract : We study several properties of Learning Vector Quantization (LVQ). LVQ is a nonparametric detection scheme proposed in the neural network community by Kohonen. We examine it in detail, both theoretical and experimentally, to determine its properties as a nonparametric classifier. In particular, we study the convergence of the parameter adjustment rule in LVQ, we present a modification to LVQ which results in improving he convergence of the algorithm, we show that LVQ performs as well as other classifiers on two sets of a simulations, and we show that the classification error associated with LVQ can be made arbitrarily small. (r.r.h.)