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- W2373110612 abstract "Based on the fact that the regression accuracy and generalization performance of the support vector regression(SVR)models depend on a proper setting of its parameters,a new trial was applied in parameters selection.The new optimal selection approach of SVR parameters was put forward based on ant colony optimization(ACO)algorithm.The k-fold cross-validation error was used as the fitness function of ACO.The node values in ant system were reflected by the kernel parameterσand regularization parameter C of SVR.The optimal values are equal to those of the best route which are decided by ants.Simulation results show that the optimal selection approach based on ACO has good robustness and strong global search capability.The method used for the research of modeling in the penicillin ferment process obtains higher accuracy." @default.
- W2373110612 created "2016-06-24" @default.
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- W2373110612 date "2008-01-01" @default.
- W2373110612 modified "2023-09-23" @default.
- W2373110612 title "Parameters selection and stimulation of support vector machines based on ant colony optimization algorithm" @default.
- W2373110612 hasPublicationYear "2008" @default.
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