Sciweavers

Share
COLT
2003
Springer

Learning with Rigorous Support Vector Machines

12 years 1 months ago
Learning with Rigorous Support Vector Machines
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
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where COLT
Authors Jinbo Bi, Vladimir Vapnik
Comments (0)
books