ML Anthology

Learning Relatively Small Classes

Shahar Mendelson
COLT 2001 pp. 273-288
doi:10.1007/3-540-44581-1_18 /colt/2001/mendelson2001colt-learning/

Abstract

We study the sample complexity of proper and improper learning problems with respect to different L _q loss functions. We improve the known estimates for classes which have relatively small covering numbers (log-covering numbers which are polynomial with exponent p < 2) with respect to the L ^q norm for q = 2.

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Cite

Text

Mendelson. "Learning Relatively Small Classes." Annual Conference on Computational Learning Theory, 2001. doi:10.1007/3-540-44581-1_18

Markdown

[Mendelson. "Learning Relatively Small Classes." Annual Conference on Computational Learning Theory, 2001.](https://mlanthology.org/colt/2001/mendelson2001colt-learning/) doi:10.1007/3-540-44581-1_18

BibTeX

@inproceedings{mendelson2001colt-learning,
  title     = {{Learning Relatively Small Classes}},
  author    = {Mendelson, Shahar},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {2001},
  pages     = {273-288},
  doi       = {10.1007/3-540-44581-1_18},
  url       = {https://mlanthology.org/colt/2001/mendelson2001colt-learning/}
}