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NeuroCOLT
Technical Report NC-TR-99-057
A
sharp concentration inequality with applications
Boucheron, Lugosi & Massart
Received: 20 May , 1999
Abstract
We derive a new general concentration-of-measure inequality. The concentration
inequality applies, among others, to configuration functions as defined
by Talagrand and also to combinatorial entropies such as the logarithm
of the number of increasing subsequences in a random permutation and
to Vapnik-Chervonenkis (VC) entropies. The results find direct applications
in statistical learning theory, substantiating the possibility to
use the empirical vc-entropy in penalization techniques.
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