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Online Policy Adaptation for Ensemble Classifiers
Type of publication: Conference paper
Citation: ESANN:DimitrakBengio2004b
Booktitle: 12th European Symposium on Artificial Neural Networks, ESANN 04
Year: 2004
Note: IDIAP-RR 03-69
Crossref: dimitrakbengio2003b:
Abstract: 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.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Dimitrakakis, Christos
Bengio, Samy
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Total mark: 0