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.
CITATION STYLE
Crammer, K., Dekel, O., Shalev-Shwartz, S., & Singer, Y. (2004). Online passive-aggressive algorithms. In Advances in Neural Information Processing Systems. Neural information processing systems foundation.
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