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SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

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Abstract

Support vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal support vector machine problem, study its effective aspects, and propose varieties of convex loss functions such as the standard for SVM with the absolute hinge error as well as the quadratic hinge and the Huber hinge errors. We present an iterative majorization algorithm that minimizes each of the adaptations. In addition, we show that many of the features of an SVM are also obtained by an optimal scaling approach to regression. We illustrate this with an example from the literature and do a comparison of different methods on several empirical data sets.

Keywords

Support vector machines Iterative majorization Absolute hinge error Quadratic hinge error Huber hinge error Optimal scaling 

Mathematics Subject Classification (2000)

90C30 62H30 68T05 
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This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Copyright information

© The Author(s) 2008

Authors and Affiliations

  • P. J. F. Groenen
    • 1
  • G. Nalbantov
    • 2
  • J. C. Bioch
    • 1
  1. 1.Econometric InstituteErasmus University RotterdamRotterdamThe Netherlands
  2. 2.ERIM and Econometric InstituteErasmus University RotterdamRotterdamThe Netherlands

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