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The objective of this study is to investigate the efficient determination of$C$ and $\gamma$ for Support Vector Regression with RBF or mahalanobis kernelbased on numerical and statistician considerations, which indicates theconnection between $C$ and kernels and demonstrates that the deviation ofgeometric distance of neighbour observation in mapped space effects the predictaccuracy of $\epsilon$-SVR. We determinate the arrange of $\gamma$ & $C$ andpropose our method to choose their best values.

Optimal $\gamma$ and $C$ for $\epsilon$-Support Vector Regression with RBF Kernels