logo Idiap Research Institute        
 [BibTeX] [Marc21]
Online Policy Adaptation for Ensemble Classifiers
Type of publication: Journal paper
Citation: dimitrakakis:neurocomputing:2005
Journal: Neurocomputing
Year: 2005
Note: IDIAP-RR 03-69
Crossref: dimitrakakis:rr03-69:
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.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Dimitrakakis, Christos
Bengio, Samy
Added by: [UNK]
Total mark: 0
  • dimitrakakis-neurocomputing-2005.pdf
  • dimitrakakis-neurocomputing-2005.ps.gz