The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Dynamically adapting kernels in support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Journal of Global Optimization
A constraint handling approach for the differential evolution algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Optimal training subset in a support vector regression electric load forecasting model
Applied Soft Computing
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An appropriate parameters selection can significantly affect the accuracy of support vector regression (SVR) model. In this paper, a new evolutionary approach based on Differential Evolution (DE-SVR) is developed to train the SVR model. The approach evolves automatically the optimal model parameters by the differential mutation operations. Experimental results on several real-world datasets demonstrate that, comparing with the GA-based SVR and the Grid search methods, the DE-SVR can search the optimal parameters much more rapidly with less training time to build the SVR model, and has the comparable prediction accuracy as Grid search, even better than GA-based SVR. Therefore, the new evolutionary DE-SVM approach is an efficient method for automatic parameter determination of SVR problem.