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