Support vector machines maximizing geometric margins for multi-class classification

作     者：Tatsumi, Keiji Tanino, Tetsuzo 

作者机构：Osaka Univ Grad Sch Engn Suita Osaka 5650871 Japan 

出 版 物：《TOP》 (TOP：西班牙统计学与运筹学学会杂志)

年 卷 期：2014年第22卷第3期

页      面：815-840页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Multi-class classification Support vector machine Machine learning Multiobjective optimization Second-order cone programming problem 

摘      要：Machine learning is a very interesting and important branch of artificial intelligence. Among many learning models, the support vector machine is a popular model with high classification ability which can be trained by mathematical programming methods. Since the model was originally formulated for binary classification, various kinds of extensions have been investigated for multi-class classification. In this paper, we review some existing models, and introduce new models which we recently proposed. The models are derived from the viewpoint of multi-objective maximization of geometric margins for a discriminant function, and each model can be trained by solving a second-order cone programming problem. We show that discriminant functions with high generalization ability can be obtained by these models through some numerical experiments.