This paper presents a part-versus-part decomposition method for massively parallel training of multi-class support vector machines (SVMs). By using this method, a massive multiclass classification problem is decomposed into a number of twoclass subproblems as small as needed. An important advantage of the part-versus-part method over existing popular pairwkeclassification approach is that a large-scale two-class subproblem can be further divided into a number of relatively smaller and balanced

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... wo-class subproblems, and fast training of SVMs on massive multi-class classification problems can he easily implemented in a massively parallel way. To demonstrate the eITectiveness of the proposed method, we perform simnlations on a large-scale text categorization problem. The experimental results show that the pmposed method is faster than the existing pairwise-classification approach, better generalization performance can he achieved, and the method scaler up to massive, complex multi-class classification problems.