鲁棒半监督ν-支持向量分类机

支持向量机在近十年成为机器学习的主要学习技术,而且已经成功应用到有监督学习问题中。Fung和Mangasarian利用支持向量机对于既有已标类别样本又有未知类别样本的训练集进行训练,方法主要是利用少量已标明类别的样本进行训练得到一个分类器的同时对于未标明类别的样本进行分类,使得间隔最大化。此优化问题中假定样本是精确的,而在现实生活中,样本通常带有统计误差。因此,考虑样本带有扰动信息的半监督两类分类问题,给出鲁棒半监督v-支持向量分类算法。该算法的参数v易于选择,而数值试验也表明该算法具有良好的稳定性和较好的分类结果。

ROBUST SEMI-SUPERVISED v-SUPPORT VECTOR MACHINES

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.