Parameters of Support Vector Machines Model Optimized Method Based on Genetic Algorithm

Abstract:

In order to obtain more accurate parameters of support vector machine model, using genetic algorithm to optimize the parameters is an effective method. This paper analyzes the principle of support vector machine for regression, support vector machine kernel function selection, kernel parameters, penalty factor selection and adjustment methods, taking into account genetic algorithm is effective in solving optimization problems, proposed a method using genetic algorithm to optimize the parameters of support vector machine, which uses genetic algorithms to make cross-validation error minimized. The simulation results demonstrate the effectiveness of this method.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 1873-1876
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.1873
• Online since: July 2014
• Authors: Yu Zhen Xie*, Zhao Gang Wang, Xiao Wei Dai
• Keywords: Genetic Algorithm (GA), Kernel Function, Optimization, Support Vector Machine (SVM)

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