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).
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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.
