<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and <bold>frequency</bold> of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.</p></abstract>

This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and frequency of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.

Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

College of Mathematics and Computers Science, QinZhou University, QinZhou, China

Panjin Vocational and Technical College, Panjin, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal...

Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

Publié en ligne: 05 oct. 2015

Pages: 140 - 149

DOI: https://doi.org/10.1515/cait-2015-0047

Mots clés
Support vector machine, parameter selection, particle swarm optimization, chaotic optimization

© Huang Dong et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

Publié en ligne: 05 oct. 2015

Pages: 140 - 149

DOI: https://doi.org/10.1515/cait-2015-0047

Mots clésSupport vector machine, parameter selection, particle swarm optimization, chaotic optimization

© Huang Dong et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Support vector machine, parameter selection, particle swarm optimization, chaotic optimization