Online adaptive policies for ensemble classifiers

被引：18

作者：

Dimitrakakis, C ^{[1
]}

Bengio, S ^{[1
]}

机构：

[1] IDIAP, CH-1920 Martigny, Switzerland

来源：

NEUROCOMPUTING | 2005年 / 64卷

关键词：

neural networks; supervised learning; reinforcement learning; ensembles; mixture of experts; boosting; Q-learning;

D O I：

10.1016/j.neucom.2004.11.031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：211 / 221

页数：11

共 16 条

[11]

LU Y, 2002, MACH LEARN, V46, P361

[12] Improved generalization through explicit optimization of margins [J].