PAC-Bayesian Stochastic Model Selection
Machine Learning
Pac-bayesian generalisation error bounds for gaussian process classification
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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.