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Theoretical Views of Boosting

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Computational Learning Theory (EuroCOLT 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1572))

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Abstract

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, we briefly survey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some empirical work.

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Author information

Authors and Affiliations

  1. AT&T Labs, Shannon Laboratory, 180 Park Avenue, Room A279, Florham Park, NJ, 07932, USA

    Robert E. Schapire

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  1. Robert E. Schapire
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Editor information

Editors and Affiliations

  1. Lehrstuhl für Informatik II, Universität Dortmund, D-44221, Dortmund, Germany

    Paul Fischer

  2. Fakultät für Mathematik, Ruhr Universität Bochum, D-44780, Bochum, Germany

    Hans Ulrich Simon

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© 1999 Springer-Verlag Berlin Heidelberg

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Schapire, R.E. (1999). Theoretical Views of Boosting. In: Fischer, P., Simon, H.U. (eds) Computational Learning Theory. EuroCOLT 1999. Lecture Notes in Computer Science(), vol 1572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49097-3_1

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  • DOI: https://doi.org/10.1007/3-540-49097-3_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65701-9

  • Online ISBN: 978-3-540-49097-5

  • eBook Packages: Springer Book Archive

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