DocumentCode :
1554237
Title :
Rademacher averages and phase transitions in Glivenko-Cantelli classes
Author :
Mendelson, Shahar
Author_Institution :
Comput. Sci. Lab., Australian Nat. Univ., Canberra, ACT, Australia
Volume :
48
Issue :
1
fYear :
2002
fDate :
1/1/2002 12:00:00 AM
Firstpage :
251
Lastpage :
263
Abstract :
We introduce a new parameter which may replace the fat-shattering dimension. Using this parameter we are able to provide improved complexity estimates for the agnostic learning problem with respect to any Lp norm. Moreover, we show that if fatε(F) = O(ε-p) then F displays a clear phase transition which occurs at p=2. The phase transition appears in the sample complexity estimates, covering numbers estimates, and in the growth rate of the Rademacher averages associated with the class. As a part of our discussion, we prove the best known estimates on the covering numbers of a class when considered as a subset of Lp spaces. We also estimate the fat-shattering dimension of the convex hull of a given class. Both these estimates are given in terms of the fat-shattering dimension of the original class
Keywords :
computational complexity; learning systems; number theory; set theory; Glivenko-Cantelli classes; Rademacher averages; agnostic learning problem; convex hull; covering numbers estimates; fat-shattering dimension; growth rate; phase transitions; sample complexity estimates; subset; Australia; Displays; Neural networks; Phase estimation; Probability; Statistics; Virtual colonoscopy;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/18.971753
Filename :
971753
Link To Document :
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