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9

COLT
2003
Springer

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Learning with Rigorous Support Vector Machines

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Learning with Rigorous Support Vector Machines

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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

Jinbo Bi, Vladimir Vapnik

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COLT 2003 | Optimal Separating Hyperplane | Separating Hyperplane | VC Dimension |

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Added	06 Jul 2010
Updated	06 Jul 2010
Type	Conference
Year	2003
Where	COLT
Authors	Jinbo Bi, Vladimir Vapnik

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