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ESANN
2004

Online policy adaptation for ensemble classifiers

9 years 11 months ago
Online policy adaptation for ensemble classifiers
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put forward. The effectiveness of this approach for online learning is demonstrated by experimental results on several UCI benchmark databases.
Christos Dimitrakakis, Samy Bengio
Added 30 Oct 2010
Updated 15 Dec 2011
Type Conference
Year 2004
Where ESANN
Authors Christos Dimitrakakis, Samy Bengio
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