The second-order cone programming support vector machine (SOCP-SVM) formulations have received much attention as the robust and efficient framework for classification. In this paper, we formulate the SOCP-SVM as the convex quadratic circular cone programming support vector machine (CCP-SVM). A projection and contraction method is used to solve the CCP-SVM. Experiments on the benchmark datasets from the UCI Repository and synthetic dataset show that the projection and contraction method for the CCP-SVM needs less computation time than the primal-dual interior point method (implemented by SeDuMi) for the SOCP-SVM. In addition, the proposed method has the almost similar accuracy, F-measure values and G-mean values as the primal-dual interior point method for the linear classifiers. The proposed method for kernel-based nonlinear classifiers can obtain higher performances of accuracy, F-measure and G-mean than the primal-dual interior point method for SOCP-SVM in some datasets.

二阶锥规划支持向量机 (SOCP-SVM) 公式作为强大而有效的分类框架受到了很多关注。在本文中，我们将 SOCP-SVM 表述为凸二次圆锥规划支持向量机 (CCP-SVM)。使用投影和收缩方法来求解 CCP-SVM。对来自 UCI 存储库和合成数据集的基准数据集的实验表明，CCP-SVM 的投影和收缩方法比 SOCP-SVM 的原始对偶内点方法（由 SeDuMi 实现）需要更少的计算时间。此外，所提出的方法具有与线性分类器的原始对偶内点方法几乎相似的准确度、F-测量值和G-均值。