PAC-Bayesian Learning of Linear Classifiers

author: Mario Marchand, Département d'informatique et de génie logiciel, Université Laval
published: Aug. 26, 2009,   recorded: June 2009,   views: 4106


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

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