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Data Discrimination via Nonlinear Generalized Support Vector Machines

  • O. L. Mangasarian
  • David R. Musicant
Part of the Applied Optimization book series (APOP, volume 50)

Abstract

The main purpose of this paper is to show that new formulations of support vector machines can generate nonlinear separating surfaces which can discriminate between elements of a given set better than a linear surface. The principal approach used is that of generalized support vector machines (GSVMs) [21] which employ possibly indefinite kernels. The GSVM training procedure is carried out by either a simple successive overrelaxation (SOR) [22] iterative method or by linear programming. This novel combination of powerful support vector machines [28, 7] with the highly effective SOR computational algorithm [19, 20, 17], or with linear programming, allows us to use a nonlinear surface to discriminate between elements of a dataset that belong to one of two categories. Numerical results on a number of datasets show improved testing set correctness, by as much as a factor of two, when comparing the nonlinear GSVM surface to a linear separating surface.

Keywords

Support Vector Machine Linear Complementarity Problem Linear Kernel Linear Programming Formulation Convex Quadratic Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • O. L. Mangasarian
    • 1
  • David R. Musicant
    • 1
  1. 1.Computer Sciences DepartmentUniversity of WisconsinMadisonUSA

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