Abstract:Abstract-In this paper we present a novel method, Random Partition based SVM (RPSVM), for speeding up SVM training. Instead of clustering the training data prior to training, RPSVM randomly partitions the training data into several clusters and then uses the centers of the clusters to train an initial SVM. This trained SVM is used to find critical clusters which are located on the decision boundary. The same procedure is applied repeatedly to each of the critical clusters, resulting in a refined SVM which cons… Show more
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