Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
This addresses the challenge of improving generalization and accuracy for ensemble classifiers, though it appears incremental as it builds on existing PAC-Bayes methods.
The paper tackles the problem of learning stochastic majority votes by minimizing a PAC-Bayes generalization bound, achieving state-of-the-art accuracy with non-vacuous tight bounds in numerical experiments.
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective. The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives -- both with uninformed (data-independent) and informed (data-dependent) priors.