Optimization of Kernel Function Parameters SVM Based on the GA

Jian Hua Zhang

doi:10.4028/www.scientific.net/AMR.433-440.4124

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Optimization of Kernel Function Parameters SVM...

Optimization of Kernel Function Parameters SVM Based on the GA

Abstract:

That Support Vector Machines applies to image recognition have good results，But the kernel function C and parameters of the SVM which influence the result and performance has not been decided. Against this question, this paper bring forward a new algorithm that combines SVM with GA to classify and uses GA to select excellent kernel function, the results of experiment show Image Recognition based on SVM and GA are effective.

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Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

4124-4128

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.4124

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Online since:

January 2012

Authors:

Jian Hua Zhang

Keywords:

Genetic Algorithm (GA), Pattern Recognition, Support Vector Machine (SVM)

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