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Online multiclass boosting
Young H Jung · Jack Goetz · Ambuj Tewari

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #11

Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings. However, the multiclass extension is in the batch setting and the online extensions only consider binary classification. We fill this gap in the literature by defining, and justifying, a weak learning condition for online multiclass boosting. This condition leads to an optimal boosting algorithm that requires the minimal number of weak learners to achieve a certain accuracy. Additionally, we propose an adaptive algorithm which is near optimal and enjoys an excellent performance on real data due to its adaptive property.

Author Information

Young H Jung (Universith of Michigan)
Jack Goetz (University of Michigan)
Ambuj Tewari (University of Michigan)

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