Optimized learning vector quantization classifier with an adaptive euclidean distance

Abstract

This paper presents a classifier based on Optimized Learning Vector Quantization (optimized version of the basic LVQ1) and an adaptive Euclidean distance. The classifier furnishes discriminative class regions of the input data set that are represented by prototypes. In order to compare prototypes and patterns, the classifier uses an adaptive Euclidean distance that changes at each iteration but is the same for all the class regions. Experiments with real and synthetic data sets demonstrate the usefulness of this classifier. © 2009 Springer Berlin Heidelberg.