Entropy Numbers of Linear Function Classes
Abstract
This paper collects together a miscellany of results originally motivated by the analysis of the generalization performance of the "maximum-margin" algorithm due to Vapnik and others. The key feature of the paper is its operator-theoretic viewpoint. New bounds on covering numbers for classes related to Maximum Margin classes are derived directly without making use of a combinatorial dimension such as the VC-dimension. Specific contents of the paper include: a new and self-contained proof of Maurey's theorem and some generalizations with small explicit values of constants; bounds on the covering numbers of maximum margin classes suitable for the analysis of their generalization performance; the extension of such classes to those induced by balls in quasi-Banach spaces (such as ` p - norms with 0 < p < 1). extension of results on the covering numbers of convex hulls of basis functions to p-convex hulls (0 < p 1); an appendix containing the tightest known bounds on the e...
Cite
Text
Williamson et al. "Entropy Numbers of Linear Function Classes." Annual Conference on Computational Learning Theory, 2000. doi:10.5555/648299.761588Markdown
[Williamson et al. "Entropy Numbers of Linear Function Classes." Annual Conference on Computational Learning Theory, 2000.](https://mlanthology.org/colt/2000/williamson2000colt-entropy/) doi:10.5555/648299.761588BibTeX
@inproceedings{williamson2000colt-entropy,
title = {{Entropy Numbers of Linear Function Classes}},
author = {Williamson, Robert C. and Smola, Alexander J. and Schölkopf, Bernhard},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2000},
pages = {309-319},
doi = {10.5555/648299.761588},
url = {https://mlanthology.org/colt/2000/williamson2000colt-entropy/}
}