In this paper, a new algorithm for training support vector machines SVMs for classification problems with parallel sequential minimal optimization SMO is proposed. The selection of the working set is paralleled so that the iteration of the optimization process is reduced greatly. The experimental results show the training time of the proposed method is always less than the original SMO algorithm, and at the same time the classification accuracy is kept.
['Xinyue Wang', 'Jun Guo']
An Algorithm for Parallelizing Sequential Minimal Optimization