Bayesian support vector regression

Martin H. Law and James T. Kwok

Abstract: We show that the Bayesian evidence framework can be applied to both $\eps$-support vector regression ($\eps$-SVR) and $\nu$-support vector regression ($\nu$-SVR) algorithms. Standard SVR training can be regarded as performing level one inference of the evidence framework, while levels two and three allow automatic adjustments of the regularization and kernel parameters respectively, without the need of a validation set.

Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS), pp.239-244, Key West, Florida, USA, January 2001.


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