11th Joint International Conference on Information Sciences

Research on SVM Algorithm with Particle Swarm Optimization

Authors
Yongjie Zhai 0, Hai-li Li, Qian Zhou
Corresponding Author
Yongjie Zhai
0North China Electric Power University
Available Online December 2008.
DOI
https://doi.org/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.
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Proceedings
11th Joint International Conference on Information Sciences
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.94How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 11th Joint International Conference on Information Sciences
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.94
DO  - https://doi.org/10.2991/jcis.2008.94
ID  - Zhai2008/12
ER  -