Research on SVM Algorithm with Particle Swarm Optimization
Yongjie Zhai 0, Hai-li Li, Qian Zhou
0North China Electric Power University
Available Online December 2008.
- https://doi.org/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.
- 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 -