Research on SVM Algorithm with Particle Swarm Optimization
- 10.2991/jcis.2008.94How to use a DOI?
- SVM; SMO; LS-SVM; PSO; selection of parameters
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.
- © 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/).
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 - 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 -