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arXiv Machine Learning By Julien Bastian (LabHC), Benjamin Leblanc (LabHC, UJM, MALICE), Pascal Germain (LabHC, UJM, MALICE), Amaury Habrard (LabHC, UJM, MALICE), Guillaume Metzler (ERIC), Emilie Morvant (LabHC), Paul Viallard (MALT)

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

Read the original on arXiv Machine Learning →

The paper introduces a derandomization framework for stochastic majority vote classifiers, converting PAC‑Bayesian guarantees into deterministic majority vote guarantees. By applying disintegrated PAC‑Bayesian theory to the space of vote weight vectors, the authors derive two families of high‑probability generalization bounds for both data‑independent and data‑dependent ensembles. These bounds naturally lead to a self‑bounding learning algorithm that optimizes deterministic majority vote performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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