@inproceedings{605eab14fcb84bee815875b18eb0d909,

title = "Geometric methods in the analysis of Glivenko-Cantelli classes",

abstract = "We use geometric methods to investigate several fundamental problems in machine learning. We present a new bound on the Lp coveringn umbers of Glivenko-Cantelli classes for 1 ≤ p < ∞ in terms of the fat-shatteringdimension of the class, which does not depend on the size of the sample. Usingthe new bound, we improve the known sample complexity estimates and bound the size of the Sufficient Statistics needed for Glivenko-Cantelli classes.",

author = "Shahar Mendelson",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001 ; Conference date: 16-07-2001 Through 19-07-2001",

year = "2001",

doi = "10.1007/3-540-44581-1_17",

language = "English",

isbn = "9783540423430",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "256--272",

editor = "David Helmbold and Bob Williamson",

booktitle = "Computational Learning Theory - 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Proceedings",

address = "Germany",

}