%0 Journal Article
%T ROBUST SEMI-SUPERVISED v-SUPPORT VECTOR MACHINES<br>鲁棒半监督ν-支持向量分类机
%A XU Honggui ZHAO Kun TIAN Yingjie
%A <br>许洪贵
%A 赵琨
%A 田英杰
%J 系统科学与数学
%D 2010
%I 
%X 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.
%K Support vector machines
%K second order cone programming
%K semi-supervised learning
%K robust<br>支持向量机
%K 二阶锥规划
%K 半监督学习
%K 鲁棒.
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=37F46C35E03B4B86&jid=0CD45CC5E994895A7F41A783D4235EC2&aid=59EF3AD4F488183A46BBC55C210E2233&yid=140ECF96957D60B2&vid=340AC2BF8E7AB4FD&iid=0B39A22176CE99FB&sid=FDC7AF55F77D8CD4&eid=9F8C5EF901EA1E7E&journal_id=1000-0577&journal_name=系统科学与数学&referenced_num=0&reference_num=18