ROBUST SEMI-SUPERVISED v-SUPPORT VECTOR MACHINES
鲁棒半监督ν-支持向量分类机

Support Vector Machines have been a dominant learning technique for almost ten years, moreover they have been applied to supervised learning problems. To use the support vector method, assume that training data in the optimization problems are known exactly. But in fact, the training data are usually subjectto measurement noise. In this paper, a robust semi-supervised classification algorithm based on linear $\nu$-Support Vector Machines is presented. Numerical simulation shows the robustness of the proposed method.