A sharp concentration inequality with applications

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dc.contributor.authorBoucheron, Stéphaneca
dc.contributor.authorLugosi, Gáborca
dc.contributor.authorMassart, Pascalca
dc.contributor.otherUniversitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.accessioned2017-07-26T10:50:11Z
dc.date.available2017-07-26T10:50:11Z
dc.date.issued1999-04-01
dc.date.modified2017-07-23T02:04:27Z
dc.description.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.
dc.format.mimetypeapplication/pdfca
dc.identifierhttps://econ-papers.upf.edu/ca/paper.php?id=376
dc.identifier.citationRandom Structures and Algorithms, 16, (2000), pp. 277-292
dc.identifier.urihttp://hdl.handle.net/10230/593
dc.language.isoeng
dc.relation.ispartofseriesEconomics and Business Working Papers Series; 376
dc.rightsL'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.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.keywordconcentration of measure
dc.subject.keywordvapnik-chervonenkis dimension
dc.subject.keywordlogarithmic sobolev inequalities
dc.subject.keywordlongest monotone subsequence
dc.subject.keywordmodel selection
dc.subject.keywordStatistics, Econometrics and Quantitative Methods
dc.titleA sharp concentration inequality with applicationsca
dc.typeinfo:eu-repo/semantics/workingPaper

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