A primal dual method for SVM training
Training support vector machines (SVM) consists of solving a convex quadratic problem (QP) with one linear equality and box constraints. In this paper, we solve this QP by a primal dual approach that combines the adaptive method with an interior point method. To initialize the algorithm, a procedure of an interior point method is used to construct an initial support. The proposed approach provides an efficient implementation of a new algorithm that exploits the advantage of the adaptive method for training SVM problems. It is based on the principle of the support and the suboptimality estimate. Experimental results confirm the efficiency of our approach over state of the art SVM algorithms such as SMO, LIBSVM and SVMLight for medium sized problems.
Neurocomputing