Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

Anil Goyal 1, 2 Emilie Morvant 2 Pascal Germain 3 Massih-Reza Amini 1
3 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
Type de document :
Pré-publication, Document de travail
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Contributeur : Anil Goyal <>
Soumis le : lundi 27 août 2018 - 13:39:41
Dernière modification le : mercredi 14 novembre 2018 - 14:40:11
Document(s) archivé(s) le : mercredi 28 novembre 2018 - 16:49:08


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  • HAL Id : hal-01857463, version 2
  • ARXIV : 1808.05784


Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini. Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. 2018. 〈hal-01857463v2〉



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