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Author Mendelson, Shahar
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Empirical risk minimization ♦ Empirical processes ♦ Small-ball method
Abstract We obtain sharp bounds on the estimation error of the Empirical Risk Minimization procedure, performed in a convex class and with respect to the squared loss, without assuming that class members and the target are bounded functions or have rapidly decaying tails. Rather than resorting to a concentration-based argument, the method used here relies on a “small-ball” assumption and thus holds for classes consisting of heavy-tailed functions and for heavy-tailed targets. The resulting estimates scale correctly with the “noise level” of the problem, and when applied to the classical, bounded scenario, always improve the known bounds.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-06-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 62
Issue Number 3
Page Count 25
Starting Page 1
Ending Page 25


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Source: ACM Digital Library