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International Conference on Algorithmic Learning Theory

ALT 2005: Algorithmic Learning Theory pp 429–443Cite as

Non-asymptotic Calibration and Resolution

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3734))

Abstract

We analyze a new algorithm for probability forecasting of binary labels, without making any assumptions about the way the data is generated. The algorithm is shown to be well calibrated and to have high resolution for big enough data sets and for a suitable choice of its parameter, a kernel on the Cartesian product of the forecast space [0,1] and the object space. Our results are non-asymptotic: we establish explicit inequalities for the performance of the algorithm.

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Author information

Authors and Affiliations

  1. Computer Learning Research Centre, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, England

    Vladimir Vovk

Authors
  1. Vladimir Vovk
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Editor information

Editors and Affiliations

  1. School of Computing, National University of Singapore, 117590, Singapore

    Sanjay Jain

  2. Ruhr-Universität Bochum, Germany

    Hans Ulrich Simon

  3. Department of Information and Communication Engineering, Faculty of Electro-Communications, The University of Electro-Communications, Chofugaoka 1–5–1, Chofu, 182-8585, Tokyo, Japan

    Etsuji Tomita

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© 2005 Springer-Verlag Berlin Heidelberg

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Vovk, V. (2005). Non-asymptotic Calibration and Resolution. In: Jain, S., Simon, H.U., Tomita, E. (eds) Algorithmic Learning Theory. ALT 2005. Lecture Notes in Computer Science(), vol 3734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564089_33

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  • DOI: https://doi.org/10.1007/11564089_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29242-5

  • Online ISBN: 978-3-540-31696-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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