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Ensembles of SVMs using an Information Theoretic Criterion

Meynet, Julien  
•
Thiran, Jean-Philippe  
2008

Training Support Vector Machine can become very challenging in large scale problems. Training several lower complexity SVMs on local subsets of the training set can significantly reduce the training complexity and also improve the classification performances. In order to obtain efficient multiple classifiers systems, classifiers need to be both diverse and individually accurate. In this paper we propose an algorithm for training ensembles of SVMs by taking into account diversity between each parallel classifier. For this, we use an information theoretic criterion that expresses a trade-off between individual accuracy and diversity. The parallel SVMs are trained jointly using an adaptation of the Kernel-Adatron algorithm for learning online multiple SVMs. The results are compared to standard multiple SVMs techniques on reference large scale datasets.

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Type
report
Author(s)
Meynet, Julien  
Thiran, Jean-Philippe  
Date Issued

2008

Subjects

pattern recognition

•

support vector machines

•

combining classifiers

•

information theory

•

lts5

Written at

EPFL

EPFL units
LTS5  
Available on Infoscience
February 22, 2008
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/18977
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