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Mixability and the Existence of Weak 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

This paper investigates the behaviour of the constant c(β) from the Aggregating Algorithm. Some conditions for mixability are derived and it is shown that for many non-mixable games c(β) still converges to 1. The condition c(β) → 1 is shown to imply the existence of weak predictive complexity and it is proved that many games specify complexity up to √n.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK

    Yuri Kalnishkan & Michael V. Vyugin

Authors
  1. Yuri Kalnishkan
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  2. Michael V. Vyugin
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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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Kalnishkan, Y., Vyugin, M.V. (2002). Mixability and the Existence of Weak 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_8

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  • DOI: https://doi.org/10.1007/3-540-45435-7_8

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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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