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conference paper not in proceedings
Online Policy Adaptation for Ensemble Classifiers
2003
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.
Type
conference paper not in proceedings
Authors
Publication date
2003
Publisher
Note
Accepted for publication in ESANN 2004
Peer reviewed
REVIEWED
EPFL units
Event name | Event place | Event date |
Bruges (Belgium) | 28-29-30 April 2004 | |
Available on Infoscience
February 7, 2011
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