PAC-Bayesian Learning of Linear Classifiers
published: Aug. 26, 2009, recorded: June 2009, views: 4102
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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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