Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653568 | Neurocomputing | 2005 | 11 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Christos Dimitrakakis, Samy Bengio,