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

17 Aug 2018Anil GoyalEmilie MorvantPascal GermainMassih-Reza Amini

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... (read more)

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