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. 1

Keywords

AlgorithmMargin (machine learning)Computer scienceSimple (philosophy)Lemma (botany)Series (stratigraphy)Binary numberCategorizationArtificial intelligenceMathematicsMachine learning

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Publication Info

Year: 2006
Type: article
Volume: 7
Issue: 19
Pages: 551-585
Citations: 1449
Access: Closed

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APA Style

                            
                                    Koby Crammer, 
                                
                                    Ofer Dekel, 
                                
                                    Joseph Keshet
                                
                                et al.
                            
                            (2006). 
                            Online Passive-Aggressive Algorithms. 
                            
                            , 7
                            (19)
                            , 551-585.