A Parametrization Scheme for Classifying Models of Learnability
Abstract
We present a systematic framework for classifying, comparing and defining models of computational learnability. Apart from the obvious ‘uniformity’ parameters we present a novel ‘solid learnability’ notion that captures the difference between ‘Guess and Test’ learning algorithms and learnability notions for which consistency with the samples guarantees success.
Cite
Text
Ben-David et al. "A Parametrization Scheme for Classifying Models of Learnability." Annual Conference on Computational Learning Theory, 1989. doi:10.1006/inco.1995.1094Markdown
[Ben-David et al. "A Parametrization Scheme for Classifying Models of Learnability." Annual Conference on Computational Learning Theory, 1989.](https://mlanthology.org/colt/1989/bendavid1989colt-parametrization/) doi:10.1006/inco.1995.1094BibTeX
@inproceedings{bendavid1989colt-parametrization,
title = {{A Parametrization Scheme for Classifying Models of Learnability}},
author = {Ben-David, Shai and Benedek, Gyora M. and Mansour, Yishay},
booktitle = {Annual Conference on Computational Learning Theory},
year = {1989},
pages = {285-302},
doi = {10.1006/inco.1995.1094},
url = {https://mlanthology.org/colt/1989/bendavid1989colt-parametrization/}
}