On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm

Weighted majority votes are central to many successful ensemble methods. PAC-Bayesian theory provides tight generalization guarantees for such models by analyzing the expected risk of stochastic classifiers, while analyzing the risk of deterministic majority votes relies on surrogate bounds. To avoi