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A sharp concentration inequality with applications

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Author Info

  • Stéphane Boucheron
  • Gábor Lugosi

    ()

  • Pascal Massart

Abstract

We present a new general concentration-of-measure inequality and illustrate its power by applications in random combinatorics. The results find direct applications in some problems of learning theory.

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File URL: http://www.econ.upf.edu/docs/papers/downloads/376.pdf
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Bibliographic Info

Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 376.

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Date of creation: Apr 1999
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Handle: RePEc:upf:upfgen:376

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Web page: http://www.econ.upf.edu/

Related research

Keywords: Concentration of measure; Vapnik-Chervonenkis dimension; logarithmic Sobolev inequalities; longest monotone subsequence; model selection;

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References

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  1. Gábor Lugosi & Andrew B. Nobel, 1998. "Adaptive model selection using empirical complexities," Economics Working Papers 323, Department of Economics and Business, Universitat Pompeu Fabra.
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Cited by:
  1. Olivier Bousquet, 2003. "New approaches to statistical learning theory," Annals of the Institute of Statistical Mathematics, Springer, vol. 55(2), pages 371-389, June.

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