A PAC-Bayesian terminal in hope and its extension to multiview learning.

Summary We propose a PAC-Bayesian theorem expressed as a bound in expectation, whereas classical PAC-Bayesian bounds are probabilistic bounds. Our main result is therefore a generalization bound on the expectation of the final majority vote. We then use this result to study multiview learning when we want to learn a model in two steps: (i) learning one or more majority votes for each view, (ii) which we combine in a second step. Finally, we empirically validate the interest of this PAC-Bayesian approach for multi-view learning.