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.
Accepted for publication in ESANN 2004
Record created on 2011-02-07, modified on 2016-08-09