Optimal $\gamma$ and $C$ for $\epsilon$-Support Vector Regression with RBF Kernels
Machine Learning
2015-06-15 v1 Machine Learning
Abstract
The objective of this study is to investigate the efficient determination of and for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between 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 & and propose our method to choose their best values.
Cite
@article{arxiv.1506.03942,
title = {Optimal $\gamma$ and $C$ for $\epsilon$-Support Vector Regression with RBF Kernels},
author = {Longfei Lu},
journal= {arXiv preprint arXiv:1506.03942},
year = {2015}
}