Bagging equalizes influence.

Bagging constructs an estimator by averaging predictors trained on bootstrap samples. Bagged estimates almost consistently improve on the original predictor. It is thus important to understand the reasons for this success, and also for the occasional failures. It is widely believed that bagging is e...

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Bibliographic Details
Published in:Machine learning. 55, 3 (2004).
Main Author: Grandvalet, Yves
Format: Article
Language:English
Subjects: