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)

Vol. 64 1-4 SPEC. ISS. 211-221

Areas of Advance

Information and Communication Technology

Subject Categories

Computer and Information Science

DOI

10.1016/j.neucom.2004.11.031

More information

Created

10/6/2017