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Article Dans Une Revue Neurocomputing Année : 2019

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

Résumé

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.
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Dates et versions

hal-01857463 , version 1 (16-08-2018)
hal-01857463 , version 2 (27-08-2018)
hal-01857463 , version 3 (07-05-2019)
hal-01857463 , version 4 (26-05-2019)

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Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini. Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. Neurocomputing, In press, ⟨10.1016/j.neucom.2019.04.072⟩. ⟨hal-01857463v3⟩
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