Online Passive-Aggressive Algorithms
Abstract
We present a unified view for online classification, regression, and uni- class problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. A conversion of our main online algorithm to the setting of batch learning is also dis- cussed. The end result is new algorithms and accompanying loss bounds for the hinge-loss.
Cite
Text
Shalev-shwartz et al. "Online Passive-Aggressive Algorithms." Neural Information Processing Systems, 2003.Markdown
[Shalev-shwartz et al. "Online Passive-Aggressive Algorithms." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/shalevshwartz2003neurips-online/)BibTeX
@inproceedings{shalevshwartz2003neurips-online,
title = {{Online Passive-Aggressive Algorithms}},
author = {Shalev-shwartz, Shai and Crammer, Koby and Dekel, Ofer and Singer, Yoram},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {1229-1236},
url = {https://mlanthology.org/neurips/2003/shalevshwartz2003neurips-online/}
}