Bayesian Support Vector Regression
Abstract
We show that the Bayesian evidence framework can be applied to both $\epsilon$-support vector regression ($\epsilon$-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.
Cite
Text
Law and Kwok. "Bayesian Support Vector Regression." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.Markdown
[Law and Kwok. "Bayesian Support Vector Regression." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/law2001aistats-bayesian/)BibTeX
@inproceedings{law2001aistats-bayesian,
title = {{Bayesian Support Vector Regression}},
author = {Law, Martin H. C. and Kwok, James Tin-Yau},
booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
year = {2001},
pages = {162-167},
volume = {R3},
url = {https://mlanthology.org/aistats/2001/law2001aistats-bayesian/}
}