Toward Efficient Agnostic Learning
Abstract
In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation.
Cite
Text
Kearns et al. "Toward Efficient Agnostic Learning." Annual Conference on Computational Learning Theory, 1992. doi:10.1145/130385.130424Markdown
[Kearns et al. "Toward Efficient Agnostic Learning." Annual Conference on Computational Learning Theory, 1992.](https://mlanthology.org/colt/1992/kearns1992colt-efficient/) doi:10.1145/130385.130424BibTeX
@inproceedings{kearns1992colt-efficient,
title = {{Toward Efficient Agnostic Learning}},
author = {Kearns, Michael J. and Schapire, Robert E. and Sellie, Linda},
booktitle = {Annual Conference on Computational Learning Theory},
year = {1992},
pages = {341-352},
doi = {10.1145/130385.130424},
url = {https://mlanthology.org/colt/1992/kearns1992colt-efficient/}
}