PAC-Bayesian learning of linear classifiers
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand · 2009
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.