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Abstract:
In this paper, we present Committee, a new multi-class
learning algorithm related to the Winnow family of algorithms.
Committee is an algorithm for combining the predictions of a set of
sub-experts in the on-line mistake-bounded model of learning. A
sub-expert is a special type of attribute that predicts with a
distribution over a finite number of classes. Committee learns a
linear function of sub-experts and uses this function to make class
predictions. We provide bounds for Committee that show it performs
well when the prediction target can be represented by a few
relevant sub-experts. We also show how Committee can be used to
solve more traditional problems composed of attributes. This leads
to a natural extension of Committee that learns on multi-class
problems that contain both traditional attributes and
sub-experts.
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