Reduced set support vector machines: Application for 2-dimensional datasets

Abstract

This paper presents the performance of the reduced set (RS) method to approximate the decision boundary for standard support vector machines (SVM) classifier without affecting its generalization performance. The main focus of this work is to demonstrate the capability of the RS method such that even with fewer set of vectors, the generalization performance is not affected. In evaluating the RS method performance, decision boundaries obtained using RS method were benchmarked against the decision boundaries obtained from the standard SVM using sequential minimal optimization (SMO) method. Specifically, the generalization ability of the two methods is not evaluated since the main objective is to analyze the effect of reduced set vector in producing approximation of SVM decision rules. Results obtained demonstrated that the SVM classifier using RS method is comparable with the standard SVM using SMO method. In fact, the RS method is better since it uses fewer set of vectors to produce similar decision boundaries while maintaining the generalization performances.