The coordinate descent method with stochastic optimization for linear support vector machines

Optimizing the training speed of support vector machines (SVMs) is one of the most important topics in the SVM research. In this paper, we propose an algorithm in which the size of working set is reduced to one in order to obtain a faster training speed. Instead of the complex heuristic criteria, the random order for selecting the elements into the working set is adopted. The proposed algorithm shows a better performance in linear SVM training, especially in the large-scale scenario.