Theoretical views of boosting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationComputational Learning Theory - 4th European Conference, EuroCOLT 1999, Proceedings
EditorsHans Ulrich Simon, Paul Fischer
PublisherSpringer Verlag
Pages1-10
Number of pages10
ISBN (Print)3540657010, 9783540657019
StatePublished - Jan 1 1999
Externally publishedYes
Event4th European Conference on Computational Learning Theory, EuroCOLT 1999 - Nordkirchen, Germany
Duration: Mar 29 1999Mar 31 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1572
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th European Conference on Computational Learning Theory, EuroCOLT 1999
CountryGermany
CityNordkirchen
Period3/29/993/31/99

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Schapire, R. E. (1999). Theoretical views of boosting. In H. U. Simon, & P. Fischer (Eds.), Computational Learning Theory - 4th European Conference, EuroCOLT 1999, Proceedings (pp. 1-10). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1572). Springer Verlag.