  We tackle the issue of classifier combinations when observations have
multiple views. Our method jointly learns view-specific weighted majority vote
classifiers (i.e. for each view) over a set of base voters, and a second
weighted majority vote classifier over the set of these view-specific weighted
majority vote classifiers. We show that the empirical risk minimization of the
final majority vote given a multiview training set can be cast as the
minimization of Bregman divergences. This allows us to derive a parallel-update
optimization algorithm for learning our multiview model. We empirically study
our algorithm with a particular focus on the impact of the training set size on
the multiview learning results. The experiments show that our approach is able
to overcome the lack of labeled information.
