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Summary: Multiclass Boosting with Repartitioning
Ling Li ling@caltech.edu
Learning Systems Group, California Institute of Technology, Pasadena, CA 91125, USA
Abstract
A multiclass classification problem can be re
duced to a collection of binary problems with
the aid of a coding matrix. The quality of the
final solution, which is an ensemble of base
classifiers learned on the binary problems, is
a#ected by both the performance of the base
learner and the errorcorrecting ability of the
coding matrix. A coding matrix with strong
errorcorrecting ability may not be overall op
timal if the binary problems are too hard for
the base learner. Thus a tradeo# between
errorcorrecting and base learning should be
sought. In this paper, we propose a new mul
ticlass boosting algorithm that modifies the
coding matrix according to the learning abil
ity of the base learner. We show experimen
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