PAC-Bayesian Theory for Transductive Learning

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Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:105-113, 2014.

Abstract

We propose a PAC-Bayesian analysis of the transductive learning setting, introduced by Vapnik [2008], by proposing a family of new bounds on the generalization error. Some of them are derived from their counterpart in the inductive setting, and others are new. We also compare their behavior.

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