%0 Journal Article
%T Maximal Margin Linear Classifier Based on the Contraction of the Closed Convex Hull<br>基于闭凸包收缩的最大边缘线性分类器
%A TAO Qing
%A SUN De-min
%A FAN Jin-song
%A FANG Ting-jian
%A <br>陶卿
%A 孙德敏
%A 范劲松
%A 方廷健
%J 软件学报
%D 2002
%I 
%X The SVM (support vector machines) is a classification technique based on the structural risk minimization principle. In this paper, another method is given to implement the structural risk minimization principle. And an exact maximal margin algorithm is proposed when classification problem is linearly separable. The linearly non-separable problem can be changed to separable linearly by using the proposed concept of the contraction of a closed convex set. The method in this paper has the same function and quality as SVM and Cortes'soft margin algorithm,but its theoretical system is simple and strict,and geometric meaning of its optimization probem is very clear and obvious.
%K closed convex set
%K contraction
%K support vectors
%K maximal margin
%K classifier<br>闭凸集
%K 收缩
%K 支持向量
%K 最大边缘
%K 分类器
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=3000172586F2632C&yid=C3ACC247184A22C1&vid=FC0714F8D2EB605D&iid=38B194292C032A66&sid=B79ACB6EBBFC9730&eid=0493D643315CD829&journal_id=1000-9825&journal_name=软件学报&referenced_num=11&reference_num=14