Multi-Order Information for Working Set Selection of Sequential Minimal Optimization

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Qimao Yang, Changrong Li, Jun Guo ;
Proceedings of Machine Learning Research, PMLR 89:3264-3272, 2019.

Abstract

A new working set selection method for sequential minimal optimization (SMO) is proposed in this paper. Instead of the method adopted in the current version of LIBSVM, which uses the second order information of the objective function to choose the violating pairs, we suggest a new method where a higher order information is considered. It includes the descent degree of the objective function and the stride of variables update. Many experimental results show, in contrast to LIBSVM, the number of iterations obtained by the proposed method is less in the vast majority of cases and the training of support vector machines (SVMs) is sped up. Meanwhile, the convergence of the proposed approach can be guaranteed and its accuracy is at the same level as LIBSVM’s.

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