Solving support vector machines beyond dual programming

Authors:
Xun Liang
Affiliations:
School of Information, Renmin University of China, Beijing, China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

Support vector machines (SV machines, SVMs) are solved conventionally by converting the convex primal problem into a dual problem with the aid of a Lagrangian function, during whose process the non-negative Lagrangian multipliers are mandatory. Consequently, in the typical C-SVMs, the optimal solutions are given by stationary saddle points. Nonetheless, there may still exist solutions beyond the stationary saddle points. This paper explores these new points violating Karush-Kuhn-Tucker (KKT) condition.