Research on SVM Algorithm with Particle Swarm Optimization

DOI

10.2991/jcis.2008.94How to use a DOI?

Keywords

SVM; SMO; LS-SVM; PSO; selection of parameters

Abstract

Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To guarantee its satisfying performance, it is important to set appropriate parameters of SVM algorithm. Sequential Minimal Optimization (SMO) is an effective training algorithm belonging to SVM, so is LS_SVM. Therefore, on the basis of the SMO algorithm and LS_SVM, we introduced Particle Swarm Optimization (PSO) algorithm, and utilized an example to certify its validity. PSO is proposed to deal with the large amount of data, and the simulation results showed the effectiveness of this method.

Copyright

© 2008, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yongjie Zhai
AU  - Hai-li Li
AU  - Qian Zhou
PY  - 2008/12
DA  - 2008/12
TI  - Research on SVM Algorithm with Particle Swarm Optimization
BT  - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
PB  - Atlantis Press
SP  - 557
EP  - 564
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.94
DO  - 10.2991/jcis.2008.94
ID  - Zhai2008/12
ER  -