Title: Research on parameters optimisation of SVM based on swarm intelligence

Authors: Shifei Ding; Huajuan Huang

Addresses: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China ' School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China; Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

Abstract: Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which has become a hot research topic in the field of machine learning because of its excellent performance. However, the performance of SVM is very sensitive to its parameters. At present, swarm intelligence is the most common method to optimise the parameters of SVM. In this paper, the research on parameters optimisation of SVM based on swarm intelligence algorithms is reviewed. Firstly, we briefly introduce the theoretical basis of SVM. Secondly, we describe the latest progress of parameters optimisation of SVM based on swarm intelligence in recent years. Finally, we point out the research and development prospects of this kind of method.

Keywords: support vector machines; SVM; parameter optimisation; swarm intelligence; machine learning.

DOI: 10.1504/IJCI.2014.064852

International Journal of Collaborative Intelligence, 2014 Vol.1 No.1, pp.4 - 17

Received: 05 Nov 2013
Accepted: 17 Dec 2013
Published online: 27 Sep 2014 *