Online multiclass learning by interclass hypothesis sharing

Michael Fink*, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations


We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but, rather, is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets.

Original languageAmerican English
Title of host publicationACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
Number of pages8
StatePublished - 2006
Event23rd International Conference on Machine Learning, ICML 2006 - Pittsburgh, PA, United States
Duration: 25 Jun 200629 Jun 2006

Publication series

NameACM International Conference Proceeding Series


Conference23rd International Conference on Machine Learning, ICML 2006
Country/TerritoryUnited States
CityPittsburgh, PA


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