Universal Kernel-Based Learning with Applications to Regular Languages
Abstract
We propose a novel framework for supervised learning of discrete concepts. Since the 1970's, the standard computational primitive has been to find the most consistent hypothesis in a given complexity class. In contrast, in this paper we propose a new basic operation: for each pair of input instances, count how many concepts of bounded complexity contain both of them.
Cite
Text
Kontorovich and Nadler. "Universal Kernel-Based Learning with Applications to Regular Languages." Journal of Machine Learning Research, 2009.Markdown
[Kontorovich and Nadler. "Universal Kernel-Based Learning with Applications to Regular Languages." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/kontorovich2009jmlr-universal/)BibTeX
@article{kontorovich2009jmlr-universal,
title = {{Universal Kernel-Based Learning with Applications to Regular Languages}},
author = {Kontorovich, Leonid and Nadler, Boaz},
journal = {Journal of Machine Learning Research},
year = {2009},
pages = {1095-1129},
volume = {10},
url = {https://mlanthology.org/jmlr/2009/kontorovich2009jmlr-universal/}
}