Multiview Boosting by Controlling the ...
Type de document :
Article dans une revue scientifique
URL permanente :
Titre :
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Auteur(s) :
Goyal, Anil [Auteur]
Morvant, Emilie [Auteur]
Germain, Pascal [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Amini, Massih-Reza [Auteur]
Morvant, Emilie [Auteur]
Germain, Pascal [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Amini, Massih-Reza [Auteur]
Titre de la revue :
Neurocomputing
Éditeur :
Elsevier
Date de publication :
2019
ISSN :
0925-2312
Mot(s)-clé(s) :
PAC-Bayes
Multiview Learning
Boosting
Multiview Learning
Boosting
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:11:46Z
2020-06-09T09:29:33Z
2020-06-09T09:29:33Z
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