{"title":"应用程序的集中度明显不平等","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":"{\"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}","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}
A sharp concentration inequality with applications
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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