John Langford recommends:

Gilles Blanchard and François Fleuret, Occam’s Hammer. When we are interested in very tight bounds on the true error rate of a classifier, it is tempting to use a PAC-Bayes bound which can (empirically) be quite tight. A disadvantage of the PAC-Bayes bound is that it applies to a classifier which is randomized over a set of base classifiers rather than a single classifier. This paper shows that a similar bound can be proved which holds for a single classifier drawn from the set. The ability to safely use a single classifier is very nice. This technique applies generically to any base bound, so it has other applications covered in the paper.

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