A sharp concentration inequality with applications
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- dc.contributor.author Boucheron, Stéphaneca
- dc.contributor.author Lugosi, Gáborca
- dc.contributor.author Massart, Pascalca
- dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
- dc.date.accessioned 2017-07-26T10:50:11Z
- dc.date.available 2017-07-26T10:50:11Z
- dc.date.issued 1999-04-01
- dc.date.modified 2017-07-23T02:04:27Z
- dc.description.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.
- dc.format.mimetype application/pdfca
- dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=376
- dc.identifier.citation Random Structures and Algorithms, 16, (2000), pp. 277-292
- dc.identifier.uri http://hdl.handle.net/10230/593
- dc.language.iso eng
- dc.relation.ispartofseries Economics and Business Working Papers Series; 376
- dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
- dc.subject.keyword concentration of measure
- dc.subject.keyword vapnik-chervonenkis dimension
- dc.subject.keyword logarithmic sobolev inequalities
- dc.subject.keyword longest monotone subsequence
- dc.subject.keyword model selection
- dc.subject.keyword Statistics, Econometrics and Quantitative Methods
- dc.title A sharp concentration inequality with applicationsca
- dc.type info:eu-repo/semantics/workingPaper