Entropy, combinatorial dimensions and random averages

Shahar Mendelson, Roman Vershynin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    6 Citations (Scopus)

    Abstract

    In this article we introduce a new combinatorial parameter which generalizes the VC dimension and the fat-shattering dimension, and extends beyond the function-class setup. Using this parameter we establish entropy bounds for subsets of the n-dimensional unit cube, and in particular, we present new bounds on the empirical covering numbers and gaussian averages associated with classes of functions in terms of the fat-shattering dimension.

    Original languageEnglish
    Title of host publicationComputational Learning Theory - 15th Annual Conference on Computational Learning Theory, COLT 2002, Proceedings
    EditorsJyrki Kivinen, Robert H. Sloan
    PublisherSpringer Verlag
    Pages14-28
    Number of pages15
    ISBN (Electronic)354043836X, 9783540438366
    DOIs
    Publication statusPublished - 2002
    Event15th Annual Conference on Computational Learning Theory, COLT 2002 - Sydney, Australia
    Duration: 8 Jul 200210 Jul 2002

    Publication series

    NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
    Volume2375
    ISSN (Print)0302-9743

    Conference

    Conference15th Annual Conference on Computational Learning Theory, COLT 2002
    Country/TerritoryAustralia
    CitySydney
    Period8/07/0210/07/02

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