A Hierarchical and Parallel Method for Training Support Vector Machines

Abstract

In order to handle large-scale pattern classification problems, various sequential and parallel classification methods have been developed according to the divide-and-conquer principle. However, existing sequential methods need long training time, and some of parallel methods lead to generalization accuracy decreasing and the number of support vectors increasing. In this paper, we propose a novel hierarchical and parallel method for training support vector machines. The simulation results indicate that our method can not only speed up training but also reduce the number of support vectors while maintaining the generalization accuracy.