The research of the parallel SMO algorithm for solving SVM

Abstract

In order to improve solving support vector machine algorithm, an improved learning algorithm of the parallel SMO is proposed. According to this algorithm, the master CPU averagely distributes primitive training set to slave CPUs so that they can almost independently run serial SMO on their respective training set. As it adopts the strategies of buffer and shrink, the speed of the parallel training algorithm is increased, which is showed in the experiments of parallel SMO based on the dataset of MNIST. The experiments indicate that the parallel SMO algorithm has good performance in solving largescale SVM.