A sharp concentration inequality with applications
AbstractWe 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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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 376.
Date of creation: Apr 1999
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Web page: http://www.econ.upf.edu/
Concentration of measure; Vapnik-Chervonenkis dimension; logarithmic Sobolev inequalities; longest monotone subsequence; model selection;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-07-28 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gábor Lugosi & Andrew B. Nobel, 1998. "Adaptive model selection using empirical complexities," Economics Working Papers 323, Department of Economics and Business, Universitat Pompeu Fabra.
- 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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