Parameters of Support Vector Machines Model Optimized Method Based on Genetic Algorithm
2014 ◽
Vol 989-994
◽
pp. 1873-1876
Keyword(s):
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.
2018 ◽
Vol 25
(35)
◽
pp. 35693-35706
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2020 ◽
Vol 27
(14)
◽
pp. 17425-17426
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2017 ◽
Vol 23
(7)
◽
pp. 739-740
◽
2017 ◽
Vol 16
(4)
◽
pp. 773-785
◽
2021 ◽
Vol 59
(4)
◽
pp. 825-839
Keyword(s):