{"title":"A sharp concentration inequality with applications","authors":"S. Boucheron, G. Lugosi, P. Massart","doi":"10.1002/(SICI)1098-2418(200005)16:3%3C277::AID-RSA4%3E3.0.CO;2-1","DOIUrl":null,"url":null,"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.","PeriodicalId":303496,"journal":{"name":"Random Struct. Algorithms","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"170","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Random Struct. Algorithms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/(SICI)1098-2418(200005)16:3%3C277::AID-RSA4%3E3.0.CO;2-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 170
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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