2003 | OriginalPaper | Chapter
Boosting with Diverse Base Classifiers
Authors : Sanjoy Dasgupta, Philip M. Long
Published in: Learning Theory and Kernel Machines
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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We establish a new bound on the generalization error rate of the Boost-by-Majority algorithm. The bound holds when the algorithm is applied to a collection of base classifiers that contains a “diverse” subset of “good” classifiers, in a precisely defined sense. We describe cross-validation experiments that suggest that Boost-by-Majority can be the basis of a practically useful learning method, often improving on the generalization of AdaBoost on large datasets.