Data Discrimination via Nonlinear Generalized Support Vector Machines (1999)
| Venue: | Complementarity: Applications, Algorithms and Extensions |
| Citations: | 12 - 8 self |
BibTeX
@TECHREPORT{Mangasarian99datadiscrimination,
author = {O. L. Mangasarian and David R. Musicant},
title = {Data Discrimination via Nonlinear Generalized Support Vector Machines},
institution = {Complementarity: Applications, Algorithms and Extensions},
year = {1999}
}
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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) which employ possibly indefinite kernels [17]. The GSVM training procedure is carried out by either the simple successive overrelaxation (SOR) [18] iterative method or by linear programming. This novel combination of powerful support vector machines [24, 5] with the highly effective SOR computational algorithm [15, 16, 14] 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. 1 Introduction A very simple convex qu...
