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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2015 Vol.7(1): 29-33 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2015.V7.925

Accelerating the Training Process of Support Vector Machines by Random Partition

Hongzhi Xu, Chunping Li, Li Li, and Hongyu Shi
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 consists of the supporting vectors in the initial round of training and those in the repeated round. This procedure is repeated recursively until no critical cluster exists, resulting in the final SVM. Our experiments on synthetic and real data sets have shown that RPSVM is indeed scalable to large data sets while the high performance is retained.

Index Terms—Support vector machine, SVM training, classification, supervised learning.

Hongzhi Xu and Chunping Li are with School of Software, Tsinghua University, Beijing, China (e-mail: cli@ tsinghua.edu.cn).
Li Li and Hongyu Shi are with the Shannon Lab, HUAWEI Technologies CO.LTD, Beijing, China (e-mail: jollylili.li@huawei.com).


Cite:Hongzhi Xu, Chunping Li, Li Li, and Hongyu Shi, "Accelerating the Training Process of Support Vector Machines by Random Partition," International Journal of Computer Theory and Engineering vol. 7, no. 1, pp. 29-33, 2015.

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