A Novel Model of Working Set Selection for SMO Decomposition Methods

Zhendong Zhao, Lei Yuan, Yuxuan Wang, Forrest Sheng Bao, Shunyi Zhang Yanfei Sun

In the process of training Support Vector Machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.

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