Section: New Results

Axis 2: Multiview Boosting by controlling the diversity and the accuracy of view-specific voters

Participant: Pascal Germain

We present a comprehensive study of multilayer neural networks with binary activation, relying on the PAC-Bayesian We propose a boosting based multiview learning algorithm 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.

It is a joint work with Emilie Morvant from Université Jean Monnet de Saint-Etienne and with Massih-Reza Amini of Université de Grenoble, and with Anil Goyal affiliated to both institutions. This work has been published in the journal Neurocomputing [26].