A parallel training algorithm of support vector machines based on the MTC architecture

For accelerating the training speed of support vector machines (SVM), a novel ldquomulti-trifurcate cascade (MTC)rdquo architecture was proposed in this paper, which held the advantages of fast feedback, high utilization rate of nodes, and more feedback support vectors. Then, a parallel algorithm for training SVM was designed based on the MTC architecture, and it was proven to converge to the optimal solution strictly. The experimental results showed that the proposed algorithm obtained very high speedup and efficiency, and needed significantly less training time than the cascade SVM algorithm.