A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning

NeurIPS 2008 Massih AminiNicolas UsunierFrançois Laviolette

In this paper we present two transductive bounds on the risk of the majority vote estimated over partially labeled training sets. Our first bound is tight when the additional unlabeled training data are used in the cases where the voted classifier makes its errors on low margin observations and where the errors of the associated Gibbs classifier can accurately be estimated... (read more)

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