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JMLR
2006

Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization

9 years 11 months ago
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
Support vector machines utilizing the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstrained minimization of a convex differentiable piecewise-quadratic objective function in the dual space. The objective function, which has a Lipschitz continuous gradient and contains only one additional finite parameter, can be minimized by a generalized Newton method and leads to an exact solution of the support vector machine problem. The approach here is based on a formulation of a very general linear program as an unconstrained minimization problem and its application to support vector machine classification problems. The present approach which generalizes both (Mangasarian, 2004) and (Fung and Mangasarian, 2004) is also applied to nonlinear approximation where a minimal number of nonlinear kernel functions are utilized to approximate a function from a given number of function values.
Olvi L. Mangasarian
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors Olvi L. Mangasarian
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