Improved Risk Tail Bounds for On-Line Algorithms
Abstract
We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incremen(cid:173) tally on the training data. Our result is based on proof techniques that are remarkably different from the standard risk analysis based on uniform convergence arguments.
Cite
Text
Cesa-bianchi and Gentile. "Improved Risk Tail Bounds for On-Line Algorithms." Neural Information Processing Systems, 2005.Markdown
[Cesa-bianchi and Gentile. "Improved Risk Tail Bounds for On-Line Algorithms." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/cesabianchi2005neurips-improved/)BibTeX
@inproceedings{cesabianchi2005neurips-improved,
title = {{Improved Risk Tail Bounds for On-Line Algorithms}},
author = {Cesa-bianchi, Nicolò and Gentile, Claudio},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {195-202},
url = {https://mlanthology.org/neurips/2005/cesabianchi2005neurips-improved/}
}