A First-Order Decomposition Algorithm for Training Bound-Constrained Support Vector Machines

We present a new decomposition algorithm for training bound-constrained Support Vector Machines in this paper. When selecting indices into the working set, only first order derivative information of the objective function in the optimization model is required. Therefore, the resulting working set selection strategy is simple and can be implemented easily. The new algorithm is proved to be global convergent in theory. New algorithm is compared with the state-of-art package BSVM. Numerical experiments on several public data sets also validate the effectiveness and efficiency of the proposed method.