A PAC-Bayes Risk Bound for General Loss Functions

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand


We provide a PAC-Bayesian bound for the expected loss of convex combinations of classifiers under a wide class of loss functions (which includes the exponential loss and the logistic loss). Our numerical experiments with Adaboost indicate that the proposed upper bound, computed on the training set, behaves very similarly as the true loss estimated on the testing set.