2003 | OriginalPaper | Chapter
Learning with Rigorous Support Vector Machines
Authors : Jinbo Bi, Vladimir N. Vapnik
Published in: Learning Theory and Kernel Machines
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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We examine the so-called rigorous support vector machine (RSVM) approach proposed by Vapnik (1998). The formulation of RSVM is derived by explicitly implementing the structural risk minimization principle with a parameter H used to directly control the VC dimension of the set of separating hyperplanes. By optimizing the dual problem, RSVM finds the optimal separating hyperplane from a set of functions with VC dimension approximate to H2+1. RSVM produces classifiers equivalent to those obtained by classic SVMs for appropriate parameter choices, but the use of the parameter H facilitates model selection, thus minimizing VC bounds on the generalization risk more effectively. In our empirical studies, good models are achieved for an appropriate H2 ∈ [5% ℓ, 30% ℓ] where ℓ is the size of training data.