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On Margins and Generalisation for Voting Classifiers - MaRDI portal

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On Margins and Generalisation for Voting Classifiers

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Publication:6401600

arXiv2206.04607MaRDI QIDQ6401600

Author name not available (Why is that?)

Publication date: 9 June 2022

Abstract: 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.




Has companion code repository: https://github.com/vzantedeschi/dirichlet-margin-bound








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