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Bagging, Boosting and the Random Subspace Method for Linear Classifiers

โœ Scribed by Marina Skurichina; Robert P. W. Duin


Publisher
Springer-Verlag
Year
2002
Tongue
English
Weight
374 KB
Volume
5
Category
Article
ISSN
1433-7541

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In classifier combination, it is believed that diverse ensembles have a better potential for improvement on the accuracy than nondiverse ensembles. We put this hypothesis to a test for two methods for building the ensembles: Bagging and Boosting, with two linear classifier models: the nearest mean c