Pascal Germain, Researcher in Machine Learning

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We present a general PAC-Bayes theorem from which all known PAC-Bayes risk bounds
are obtained as particular cases. We also propose different learning algorithms for 
finding linear classifiers that minimize these bounds. These learning algorithms are 
generally competitive with both AdaBoost and the SVM.

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