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

• Type: Conference
• Year: 2003
• Where: COLT
• Authors: Jinbo Bi, Vladimir Vapnik
• Keywords: COLT 2003, Optimal Separating Hyperplane, Separating Hyperplane, VC Dimension