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Localized Rademacher Complexities

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Computational Learning Theory (COLT 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2375))

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Abstract

We investigate the behaviour of global and local Rademacher averages. We present new error bounds which are based on the local averages and indicate how data-dependent local averages can be estimated without a priori knowledge of the class at hand.

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References

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Author information

Authors and Affiliations

  1. BIOwulf Technologies, Berkeley, CA, USA

    Peter L. Bartlett & Olivier Bousquet

  2. Centre de Mathématiques Appliquées, Ecole Polytechnique, 91128, Palaiseau, France

    Olivier Bousquet

  3. Research School of Information Sciences and Engineering, The Australian National University, Canberra, ACT, 0200, Australia

    Peter L. Bartlett & Shahar Mendelson

Authors
  1. Peter L. Bartlett
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  2. Olivier Bousquet
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  3. Shahar Mendelson
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Editor information

Editors and Affiliations

  1. Research School of Information Sciences and Engineering, Australian National University, Canberra, ACT, 0200, Australia

    Jyrki Kivinen

  2. Computer Science Department, University of Illinois at Chicago, 851 S. Morgan St., Chicago, IL, 60607, USA

    Robert H. Sloan

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© 2002 Springer-Verlag Berlin Heidelberg

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Bartlett, P.L., Bousquet, O., Mendelson, S. (2002). Localized Rademacher Complexities. In: Kivinen, J., Sloan, R.H. (eds) Computational Learning Theory. COLT 2002. Lecture Notes in Computer Science(), vol 2375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45435-7_4

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  • DOI: https://doi.org/10.1007/3-540-45435-7_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43836-6

  • Online ISBN: 978-3-540-45435-9

  • eBook Packages: Springer Book Archive

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