BCP and ZQP Strategies to Reduce the SVM Training Time

The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not ...

This paper presents a comparison of different initialization algorithms joint with decomposition methods, in order to reduce the training time of Support Vector Machines (SVMs). Training a SVM involves the solution of a quadratic optimization problem (...

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 ...