Pascal Germain, Researcher in Machine Learning

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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.

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