Parallel Randomized Support Vector Machine
A parallel support vector machine based on randomized sampling technique is proposed in this paper. We modeled a new LP-type problem so that it works for general linear-nonseparable SVM training problems unlike the previous work . A unique priority based sampling mechanism is used so that we can prove an average convergence rate that is so far the fastest bounded convergence rate to the best of our knowledge. The numerical results on synthesized data and a real geometric database show that our algorithm has good scalability.
KeywordsSupport Vector Machine Combinatorial Dimension Training Vector Geographic Information System Database Regularization Factor
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