%0 Journal Article
%T Theory of hypersphere multiclass SVM<br>超球体多类支持向量机理论
%A Xu Tu
%A HE Da-ke
%A <br>徐图
%A 何大可
%J 控制理论与应用
%D 2009
%I 
%X Constructed by standard binary classes support vector machine(SVM), present multiclass SVMs are usually very slow to be trained. When a large number of categories of data are to be classified, the training work could be very difficult. By extending the hypersphere one-class SVM(HSOC-SVM) to a hypersphere multiclass SVM(HSMC-SVM), we build a fast training classifier HSOC-SVM. Its training speed is higher than that of the present multiclass classifiers, because each category data trains only one HSOC-SVM. In order to improve the training speed for the HSMC-SVM, we propose a training algorithm based on the existing algorithm for SMO. Meanwhile, the theoretic upper bound of the generalized error of HSMC-SVM is analyzed for evaluating the general performance of HSMC-SVM. Numeric experiments show that the training speed of HSMC-SVM is especially improved when many categories of data are to be classified. Thus, HSMC-SVM provides a new idea for developing fast-directed multiclass classifiers in machine learning area.
%K support vector machine(SVM)
%K multi-class SVM
%K SMO algorithm
%K generalization performance
%K HSMC-SVM<br>支持向量机
%K 多类支持向量机
%K SMO训练算法
%K 推广性能
%K 超球体多类支持向量机
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=72DBE9A843F4D294E96F8D9BDE984B23&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=708DD6B15D2464E8&sid=B593C98A8D813A42&eid=F8085F090F1A1511&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0