A New Support Vector Machine Optimized by Simulated Annealing for Global Optimization
Автор: Jiayang Wang, Wensheng Wang, Shaogui Wu
Журнал: International Journal of Engineering and Manufacturing(IJEM) @ijem
Статья в выпуске: 1 vol.2, 2012 года.
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SA-SVM model was proposed in which parameters were optimized by simulated annealing. Parameter (the kernel function) and C (the error discipline) are the key factors to the precision of SVM. Simulated annealing was used to optimize the key parameters of SVM to make enhancement on the forecasting effect of SVM. By applying this proposed model for several function optimizations, results of which demonstrate the improvement of SA-SVM on the high model accuracy in the optimization searching, and it can overcome the blindness of the model parameters.
Support vector machines, simulated annealing, global optimization, parameters optimization
Короткий адрес: https://sciup.org/15014279
IDR: 15014279
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