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论文类型：会议论文

发表时间：2018-01-01

收录刊物：CPCI-S

卷号：10862

页面范围：715-721

关键字：Support vector machine; Non-parallel SVM; Structural risk minimization principle; Dual coordinate descend algorithm

摘要：In this paper, we propose a novel large margin proximal non-parallel twin support vector machine for binary classification. The significant advantages over twin support vector machine are that the structural risk minimization principle is implemented and by adopting uncommon constraint formulation for the primal problem, the proposed method avoids the computation of the large inverse matrices before training which is inevitable in the formulation of twin support vector machine. In addition, the dual coordinate descend algorithm is used to solve the optimization problems to accelerate the training efficiency. Experimental results exhibit the effectiveness and the classification accuracy of the proposed method.