Abstract
Generalization performance of support vector machines (SVM) with Gaussian kernel is influenced by its model parameters, both the error penalty parameter and the Gaussian kernel parameter. After researching the characteristics and properties of the parameter simultaneous variation of support vector machines with Gaussian kernel by the parameter analysis table, a new area distribution model is proposed, which consists of optimal straight line, reference point of area boundary, optimal area, transition area, underfitting area, and overfitting area. In order to improve classification performance of support vector machines, a genetic algorithm based on change area search is proposed. Comparison experiments show that the test accuracy of the genetic algorithm based on change area search is better than that of the two-linear search method.
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Acknowledgments
The work was supported partially by the National Science Foundation (NSF) of China under the contract number 60702075, and the Doctoral Research Foundation of the Ministry of Education of China under the contract number 20090185120009, and the Fundamental Research Funds for the Central Universities under Grant No. ZYGX2009J057.
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Zhao, M., Ren, J., Ji, L. et al. Parameter selection of support vector machines and genetic algorithm based on change area search. Neural Comput & Applic 21, 1–8 (2012). https://doi.org/10.1007/s00521-011-0603-9
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DOI: https://doi.org/10.1007/s00521-011-0603-9