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Optimization of combined kernel function for SVM by particle swarm optimization
Lu M.-Z.2; Chen C.L.P.2; Huo J.-B.1
2009-11-17
Source PublicationProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume2
Pages1160-1166
AbstractTo choose an appropriate kernel function is one major task for SVM. Different kernel functions will produce different SVMs and may result in different performances. Combined kernel function shows more stable and higher performance than single kernel function, so there is a need to optimize the combined kernel function to enhance the generalization capability of SVM. This paper proposes to optimize the combined kernel function by Particle Swarm Optimization (PSO) based on large margin learning theory of SVM. The comparison of the performance between GA and PSO algorithm on this optimization problem is provided. The simulation results show that the PSO is another feasible solution for optimization of combined kernel function, which normally leads to SVM with better generalization capability and stability. © 2009 IEEE.
KeywordCombined kernel function Large margin learning Particle swarm optimization SVM Swarm intelligence
DOI10.1109/ICMLC.2009.5212418
URLView the original
Language英語
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Central Hospital of Shijiazhuang
2.University of Texas at San Antonio
Recommended Citation
GB/T 7714
Lu M.-Z.,Chen C.L.P.,Huo J.-B.. Optimization of combined kernel function for SVM by particle swarm optimization[C],2009:1160-1166.
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