Optimal γ and C for ε-Support Vector Regression with RBF Kernels
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The objective of this study is to investigate the efficient determination of C and γ 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 ϵ-SVR. We determinate the arrange of γ & C and propose our method to choose their best values.
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