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.


Presented at:
ESANN 2004 - 12th European Symposium On Artificial Neural Networks, Bruges (Belgium), 28-29-30 April 2004
Year:
2003
Publisher:
IDIAP
Note:
Accepted for publication in ESANN 2004
Laboratories:




 Record created 2011-02-07, last modified 2018-03-17

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