Optimal $γ$ and $C$ for $ε$-Support Vector Regression with RBF Kernels
This work addresses hyperparameter tuning for SVR users, but it appears incremental as it builds on existing SVR frameworks.
The study tackled the problem of efficiently determining the hyperparameters C and γ for ε-Support Vector Regression with RBF kernels by analyzing their connection to kernel properties and geometric distances in mapped space, resulting in a proposed method to select optimal values.
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.