A combination of modified particle swarm optimization algorithm and support vector machine for pattern classification

Authors:
Zhiming Liu;Cheng Wang;Shanzhen Yi
Affiliations:
Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science & Technology, Wuhan, China;Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science & Technology, Wuhan, China;Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science & Technology, Wuhan, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

Support Vector Machine for Pattern Classification is motivated by linear machines, but rely on preprocessing the data to represent in a high dimension with an appropriate nonlinear mapping, data from two categories can by separated by a hyperplane. To make certain the hyperplane, the key problem is selecting appropriate criterion and algorithm. To find out the appropriate solution vector in Solution Spaces, fixed increment, variable increment, relaxation, and stochastic approximation etc. may be selected, this article provide a novel method-modified general particle swarm optimization for finding the solution vector. The proposed method enhances performance and avoid over fitness effectively.