CONF ESANN:DimitrakBengio2004b/IDIAP Online Policy Adaptation for Ensemble Classifiers Dimitrakakis, Christos Bengio, Samy https://publications.idiap.ch/index.php/publications/showcite/dimitrakbengio2003b Related documents 12th European Symposium on Artificial Neural Networks, ESANN 04 2004 IDIAP-RR 03-69 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. REPORT DimitrakBengio2003b/IDIAP Online Policy Adaptation for Ensemble Classifiers Dimitrakakis, Christos Bengio, Samy EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr-03-69.pdf PUBLIC Idiap-RR-69-2003 2003 IDIAP Accepted for publication in ESANN 2004 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.