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

2015/06/12 by Lu, Longfei
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1506.03942

Abstract

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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