文献类型：期刊文献

英文题名：CANONICAL DUALITY SOLUTION FOR ALTERNATING SUPPORT VECTOR MACHINE

作者：Yuan, Yubo

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Dept Comp Sci & Technol, Shanghai 200237, Peoples R China

年份：2012

卷号：8

期号：3

起止页码：611

外文期刊名：JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000307834500007)】；

基金：This research has been supported by the National Natural Science Foundation under Grants(No. 61001200, 61101239), Natural Science Foundation and Education Department of Zhejiang under Grants(Nos.Y6100010).

语种：英文

外文关键词：Global optimization; duality theory; classification; support vector machine; data mining

摘要：Support vector machine (SVM) is one of the most popular machine learning methods and is educed from a binary data classification problem. In this paper, the canonical duality theory is used to solve the normal model of SVM. Several examples are illustrated to show that the exact solution can be obtained after the canonical duality problem being solved. Moreover, the support vectors can be located by non-zero elements of the canonical dual solution.