On the importance of small coordinate projections
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. We show that a new more general principle guarantees good generalization bounds. The new principle requires that random coordinate projections of the function class evaluated on random samples are “small” with high probability and that the random class of...[Show more]
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|Source:||Journal of Machine Learning Research|
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