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.