Fast extraction strategy of support vector machines

Abstract

As a universally accepted tool of machine learning, support vector machine (SVM) is efficent in most scenarios but often suffers from prohibitive complexity in dealing with large-scale classification problems in terms of computation time and storage space. To address such intractability, this paper presents a group and nearest neighbor strategy aiming to extract support vectors from training samples for obtaining the discriminant function in a fast fashion as only the support vectors contribute to the function. For non-linear cases, kernel function is investigated and adopted in this approach. The proposed stragtegy is described through mathematical analysis and evaluated by a set of numerical experiments. The result demonstrates that the suggested approach is effective in addressing the large-scale classification problems with acceptabe complexity. © 2011 Springer-Verlag Berlin Heidelberg.