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International Conference on Computational Learning Theory

COLT 2001: Computational Learning Theory pp 273–288Cite as

Learning Relatively Small Classes

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2111))

Abstract

We study the sample complexity of proper and improper learning problems with respect to different L q loss functions. We improve the known estimates for classes which have relatively small covering numbers (log-covering numbers which are polynomial with exponent p < 2) with respect to the L q norm for q = 2.

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References

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Author information

Authors and Affiliations

  1. Computer Sciences Laboratory, RSISE, The Australian National University, Canberra, 0200, Australia

    Shahar Mendelson

Authors
  1. Shahar Mendelson
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Editor information

Editors and Affiliations

  1. School of Engineering, Department of Computer Science, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA

    David Helmbold

  2. Research School of Information Sciences and Engineering Department of Telecommunications Engineering, Australian National University, Canberra, 0200, Australia

    Bob Williamson

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© 2001 Springer-Verlag Berlin Heidelberg

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Mendelson, S. (2001). Learning Relatively Small Classes. In: Helmbold, D., Williamson, B. (eds) Computational Learning Theory. COLT 2001. Lecture Notes in Computer Science(), vol 2111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44581-1_18

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  • DOI: https://doi.org/10.1007/3-540-44581-1_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42343-0

  • Online ISBN: 978-3-540-44581-4

  • eBook Packages: Springer Book Archive

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