Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods
Abstract
In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.
Cite
Text
Haussler et al. "Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods." Neural Information Processing Systems, 1991.Markdown
[Haussler et al. "Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/haussler1991neurips-estimating/)BibTeX
@inproceedings{haussler1991neurips-estimating,
title = {{Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods}},
author = {Haussler, David and Kearns, Michael and Opper, Manfred and Schapire, Robert},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {855-862},
url = {https://mlanthology.org/neurips/1991/haussler1991neurips-estimating/}
}