Combined parameter optimization for ε-SVR based on weighted accuracy
Aiming at the lack of integrity theories for choosing the parameters of the support vector regression machine (SVR), the combination accuracy is proposed to evaluate the estimated effect. The methods of circulation crisscross verification and variable step length are used to search the optimal parameters. The interaction of the parameters is considered. This paper researches the influence of the combined form of parameters on the estimated accuracy, and assures the optimized combined form of the parameters. The result indicates the optimized combined form of the parameters can improve the expenses estimated accuracy.
expenses estimatecirculation crisscross verificatione-support vector regression machine (ε-SVR)optimal parameterkernel function
NETL
NSTL
万方数据
维普
