One of the key factors that limit Support Vector Machines (SVMs) application in large sample problems is that the large-scale quadratic programming (QP) that arises from SVMs training cannot be easily solved via standard QP technique. The Sequential Minimal Optimization (SMO) is current one of the major methods for solving SVMs. This method, to a certain extent, can decrease the degree of difficulty of a QP problem through decomposition strategies, however, the high training price for saving memory space must be endured. In this paper, an algorithm in the light of the idea of parallel computing based on Symmetric Multiprocessor (SMP) machine is improved. The new technique has great advantage in terms of speediness when applied to problems with large training sets and high dimensional spaces without reducing generalization performance of SVMs.
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