Online Passive-Aggressive Algorithms

Abstract

We present a unified view for online classification, regression, and uniclass 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 discussed. The end result is new algorithms and accompanying loss bounds for the hinge-loss.

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Cite this paper

@article{ShalevShwartz2003OnlinePA, title={Online Passive-Aggressive Algorithms}, author={Shai Shalev-Shwartz and Koby Crammer and Ofer Dekel and Yoram Singer}, journal={Journal of Machine Learning Research}, year={2003}, volume={7}, pages={551-585} }