Support vector machines maximizing geometric margins for multi-class classification

Keiji Tatsumi () and Tetsuzo Tanino ()

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2014, vol. 22, issue 3, 815-840

Abstract: 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. Copyright Sociedad de Estadística e Investigación Operativa 2014

Keywords: Multi-class classification; Support vector machine; Machine learning; Multiobjective optimization; Second-order cone programming problem; 62H30; 90C29; 68T05 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11750-014-0338-8

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