Optimal γ and C for ε-Support Vector Regression with RBF Kernels

Abstract

The objective of this study is to investigate the efficient determination of C and 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 -SVR. We determinate the arrange of & C and propose our method to choose their best values.

Tasks

Reproductions