PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online Multiclass Learning by Interclass Hypothesis Sharing
Michael Fink, Shai Shalev-Shwartz, Yoram Singer and Shimon Ullman
In: ICML 2006, June 25-29, 2006, Pittsburgh, Pennsylvania..


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:2145
Deposited By:Shai Shalev-Shwartz
Deposited On:11 July 2006