Accelerated training of support vector machines

This paper introduces two methods to reduce the training time of large scale support vector machines (SVMs). To optimize a SVM a quadratic optimization problem has to be solved. For large scale applications with many training vectors this can only be done by splitting the data set into smaller pieces called chunks. The chunking algorithms normally start with a random subset. In this paper we propose two methods that can to find a better than a random starting subset, and therefore accelerate the optimization process. They both estimate which training vectors are likely to be support vectors in the final SVM. In the input space this is difficult to determine, because the decision surface can have an (nearly) arbitrary shape. Therefore, this is done in the high dimensional projected space of the SVM