  We study the generalisation properties of majority voting on finite ensembles
of classifiers, proving margin-based generalisation bounds via the PAC-Bayes
theory. These provide state-of-the-art guarantees on a number of classification
tasks. Our central results leverage the Dirichlet posteriors studied recently
by Zantedeschi et al. [2021] for training voting classifiers; in contrast to
that work our bounds apply to non-randomised votes via the use of margins. Our
contributions add perspective to the debate on the "margins theory" proposed by
Schapire et al. [1998] for the generalisation of ensemble classifiers.
