Online Passive-Aggressive Algorithms
- Koby Crammer ,
- Ofer Dekel ,
- Shai Shalev-Shwartz ,
- Yoram Singer
Advances in Neural Information Processing Systems '16 |
Published by MIT Press
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 discussed. The end result is new algorithms and accompanying loss bounds for the hinge-loss.