Chaos Particle Swarm Optimization Algorithm for Optimizing the Parameters of Support Vector Machine

DOI

10.2991/iccse-15.2015.5How to use a DOI?

Keywords

Support vector machine; Chaos particle swarm optimization; Parameters optimization.

Abstract

SVM parameter selection determines the performance of its learning and generalization ability. As many in the choice of the number of selectable parameters, blindly searching optimal parameters in a number of parameters are required tremendous time cost , and it is difficult to obtain the optimal parameters. To solve the support vector machine parameter optimization problem, a chaos particle swarm optimization algorithm is introduced to determine the parameters of SVM. Chaos theory is applied in PSO algorithm to improve the diversity of the population and particle traversal search, which can effectively improve the PSO algorithm convergence speed and accuracy., and SVM mode is optimized. Through a specific example, its results demonstrate that the chaos PSO has a good high efficiency and higher accuracy than the traditional PSO applied in SVM classifier.

Copyright

© 2015, 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  - Zi-de Tian
PY  - 2015/07
DA  - 2015/07
TI  - Chaos Particle Swarm Optimization Algorithm for Optimizing the Parameters of Support Vector Machine
BT  - Proceedings of the 2015 International Conference on Computational Science and Engineering
PB  - Atlantis Press
SP  - 22
EP  - 27
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-15.2015.5
DO  - 10.2991/iccse-15.2015.5
ID  - Tian2015/07
ER  -