A Bayesian Divergence Prior for Classiffier Adaptation
Abstract
Adaptation of statistical classifiers is critical when a target (or testing) distribution is different from the distribution that governs training data. In such cases, a classifier optimized for the training distribution needs to be adapted for optimal use in the target distribution. This paper presents a Bayesian “divergence prior” for generic classifier adaptation. Instantiations of this prior lead to simple yet principled adaptation strategies for a variety of classifiers, which yield superior performance in practice. In addition, this paper derives several adaptation error bounds by applying the divergence prior in the PAC-Bayesian setting.
Cite
Text
Li and Bilmes. "A Bayesian Divergence Prior for Classiffier Adaptation." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Li and Bilmes. "A Bayesian Divergence Prior for Classiffier Adaptation." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/li2007aistats-bayesian/)BibTeX
@inproceedings{li2007aistats-bayesian,
title = {{A Bayesian Divergence Prior for Classiffier Adaptation}},
author = {Li, Xiao and Bilmes, Jeff},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {275-282},
volume = {2},
url = {https://mlanthology.org/aistats/2007/li2007aistats-bayesian/}
}