Variational Bayesian Approach to Support Vector Regression
Zhuo Gao, K. Y. Michael Wong · Progress of Theoretical Physics Supplement · 2005
We consider a variational Bayesian approach to support vector regression (SVR). Its main advantage is that one can estimate the leave-one-out error of an SVR analytically without doing the cross-validation. Comparing our theory with the simulations on both artificial (the “sinc” function) and benchmark (the “Boston Housing”) datasets, we get a good agreement. Furthermore, the smoothness of the hyperparameter dependence of the leave-one-out error can be tuned, which is useful for determining the optimal hyperparameters.