A part-versus-part method for massively parallel training of support vector machines

Abstract

This work presents a part-versus-part decomposition method for massively parallel training of multi-class support vector machines (SVMs). By using this method, a massive multi-class classification problem is decomposed into a number of two-class subproblems as small as needed. An important advantage of the part-versus-part method over existing popular pair wise-classification approach is that a large-scale two-class subproblem can be further divided into a number of relatively smaller and balanced two-class subproblems, and fast training of SVMs on massive multi-class classification problems can be easily implemented in a massively parallel way. To demonstrate the effectiveness of the proposed method, we perform simulations on a large-scale text categorization problem. The experimental results show that the proposed method is faster than the existing pairwise-classification approach, better generalization performance can be achieved, and the method scales up to massive, complex multi-class classification problems.