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Xiang Chang Sheng
Hunan Institute of Engineering

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Support Vector Machine Optimized by Improved Genetic Algorithm Xiang Chang Sheng; Zhou Yu; Xilong Qu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: January 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Parameters of support vector machines (SVM) which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3182
Co-Authors Xilong Qu Zhou Yu