Benjamin Leblanc

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

A derandomization framework for stochastic majority votes is proposed that derives two families of high-probability generalization bounds, covering both data-independent and data-dependent constructions of the ensemble, which naturally lead to a self-bounding learning algorithm optimizing deterministic majority vote gu...