Online adaptive policies for ensemble classifiers
Journal article, 2005
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. © 2005 Elsevier B.V. All rights reserved.
Q-learning
Mixture of experts
Ensembles
Neural networks
Boosting
Reinforcement learning
Supervised learning
Author
Christos Dimitrakakis
Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)
S. Bengio
Neurocomputing
0925-2312 (ISSN) 18728286 (eISSN)
Vol. 64 1-4 SPEC. ISS. 211-221Areas of Advance
Information and Communication Technology
Subject Categories
Computer and Information Science
DOI
10.1016/j.neucom.2004.11.031