Random subclass bounds
It has been recently shown that sharp generalization bounds can be obtained when the function class from which the algorithm chooses its hypotheses is "small" in the sense that the Rademacher averages of this function class are small. Seemingly based on different arguments, generalization bounds were obtained in the compression scheme, luckiness, and algorithmic luckiness frameworks in which the "size" of the function class is not specified a priori. We show that the bounds obtained in all...[Show more]
|Collections||ANU Research Publications|
|Source:||Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington DC, USA, August 24-27, 2003|
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