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...
J. Bastian, Benjamin Leblanc, Pascal Germain et al.
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