Solution to Support Vector Machine Using Canonical Duality

摘要：

Support vector machine (SVM) is one of the most popular machine learning method and educed from a binary data classification problem. In this paper, a new duality theory named canonical duality theory is presented 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.

关键词： Support Vector Machine Canonical Duality Quadratic Programming

作者: Yubo Yuan Feilong Cao

作者单位: Institute of Metrology and Computational Science, China Jiliang University,Hangzhou 310018, P.R. China

会议类型: 国际会议

会议名称: The First World Congress on Global Optimization in Engineering & Science(第一届工程与科学全局优化国际会议 WCGO2009)

会议地点: 长沙

会议语种:英文

页码: 393-398

在线出版日期: 2009-06-01（万方平台首次上网日期，不代表论文的发表时间）