An Error Bound Based on a Worst Likely Assignment
Abstract
This paper introduces a new PAC transductive error bound for classification. The method uses information from the training examples and inputs of working examples to develop a set of likely assignments to outputs of the working examples. A likely assignment with maximum error determines the bound. The method is very effective for small data sets.
Cite
Text
Bax and Callejas. "An Error Bound Based on a Worst Likely Assignment." Journal of Machine Learning Research, 2008.Markdown
[Bax and Callejas. "An Error Bound Based on a Worst Likely Assignment." Journal of Machine Learning Research, 2008.](https://mlanthology.org/jmlr/2008/bax2008jmlr-error/)BibTeX
@article{bax2008jmlr-error,
title = {{An Error Bound Based on a Worst Likely Assignment}},
author = {Bax, Eric and Callejas, Augusto},
journal = {Journal of Machine Learning Research},
year = {2008},
pages = {859-891},
volume = {9},
url = {https://mlanthology.org/jmlr/2008/bax2008jmlr-error/}
}