ARTICLE dimitrakakis:neurocomputing:2005/IDIAP Online Policy Adaptation for Ensemble Classifiers Dimitrakakis, Christos Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/papers/2005/dimitrakakis-neurocomputing-2005.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/dimitrakakis:rr03-69 Related documents Neurocomputing 2005 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 we attempt to train and combine the base classifiers using an adaptive policy. This policy is learnt through a $Q$-learning inspired technique. Its effectiveness for an essentially supervised task is demonstrated by experimental results on several UCI benchmark databases. REPORT dimitrakakis:rr03-69/IDIAP Online Policy Adaptation for Ensemble Classifiers Dimitrakakis, Christos Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2003/dimitrakakis-idiap-rr-03-69.pdf PUBLIC Idiap-RR-69-2003 2003 IDIAP published in Neurocomputing