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Permutation Complexity Bound on Out-Sample Error

2020-01-06 | |  64 |   42 |   0

Abstract

We define a data dependent permutation complexity for a hypothesis set H, which is similar to a Rademacher complexity or maximum discrepancy. The permutation complexity is based (like the maximum discrepancy) on dependent sampling. We prove a uniform bound on the generalization error, as well as a concentration result which means that the permutation estimate can be efficiently estimated.

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