Rademacher and Gaussian complexities: Risk bounds and structural results

Peter L. Bartlett, Shahar Mendelson

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

    54 Citations (Scopus)

    Abstract

    We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

    Original languageEnglish
    Title of host publicationComputational Learning Theory - 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Proceedings
    EditorsDavid Helmbold, Bob Williamson
    PublisherSpringer Verlag
    Pages224-240
    Number of pages17
    ISBN (Print)9783540423430
    DOIs
    Publication statusPublished - 2001
    Event14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001 - Amsterdam, Netherlands
    Duration: 16 Jul 200119 Jul 2001

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2111
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001
    Country/TerritoryNetherlands
    CityAmsterdam
    Period16/07/0119/07/01

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