Competitive learning networks for unsupervised training

Abstract

Unsupervised training can play an important role in a hybrid classification system. Many clustering techniques such as K-means have been employed in unsupervised training. In this study competitive learning networks are proposed as unsupervised training methods. The Jeffries-Matusita (J-M) distance, which is a measure of statistical separability of pairs of the ‘trained’ classes, was used to evaluate the capability of the proposed methods. The simulation results and comparisons with K-means are provided.