A simple implementation of the stochastic discrimination for pattern recognition

Abstract

The method of stochastic discrimination (SD) introduced by Kleinberg ([6,7])is a new method in pattern recognition. It works by producing weak classifiers and then combining them via the Central Limit Theorem to form a strong classifier. SD is overtraining-resistant, has a high convergence rate, and can work quite well in practice. However, some strict assumptions involved in SD and the difficulties in understanding SD have limited its practical use. In this paper, we present a simple algorithm of SD for two-class pattern recognition. We illustrate the algorithm by applications in classifying the feature vectors from some real and simulated data sets. The experimental results show that SD is fast, effective, and applicable. © Springer-Verlag Berlin Heidelberg 2000.