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Support vector machines maximizing geometric margins for multi-class classification

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  • Keiji Tatsumi
  • Tetsuzo Tanino

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

Suggested Citation

  • Keiji Tatsumi & Tetsuzo Tanino, 2014. "Support vector machines maximizing geometric margins for multi-class classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 815-840, October.
    • Handle: RePEc:spr:topjnl:v:22:y:2014:i:3:p:815-840
    DOI: 10.1007/s11750-014-0338-8
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    References listed on IDEAS

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    1. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
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    Cited by:

    1. Martín Barragán, Belén, 2016. "A Partial parametric path algorithm for multiclass classification," DES - Working Papers. Statistics and Econometrics. WS 22390, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Corrado Coppola & Lorenzo Papa & Marco Boresta & Irene Amerini & Laura Palagi, 2024. "Tuning parameters of deep neural network training algorithms pays off: a computational study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 579-620, October.

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