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In this paper we propose a boosting based multiview learning algorithm,referred to as PB-MVBoost, which iteratively learns i) weights overview-specific voters capturing view-specific information; and ii) weights overviews by optimizing a PAC-Bayes multiview C-Bound that takes into account theaccuracy of view-specific classifiers and the diversity between the views. Wederive a generalization bound for this strategy following the PAC-Bayes theorywhich is a suitable tool to deal with models expressed as weighted combinationover a set of voters. Different experiments on three publicly availabledatasets show the efficiency of the proposed approach with respect tostate-of-art models.

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