doi: 10.7763/IJCTE.2015.V7.925
Accelerating the Training Process of Support Vector Machines by Random Partition
,
Chunping Li1
, ,
- 1School of Software, Tsinghua University, Beijing, China.
- 2Shannon Lab, HUAWEI Technologies CO.LTD, Beijing, China.
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.
Keywords
- Support vector machine
- SVM training
- classification
- supervised learning
How to 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. https://doi.org/10.7763/IJCTE.2015.V7.925
Copyright & License
Copyright © 2015 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).