Theoretical, views of boosting and applications

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

89 Scopus citations


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. Some empirical work and applications are also described.

Original languageEnglish (US)
Title of host publicationAlgorithmic Learning Theory - 10th International Conference, ALT 1999, Proceedings
EditorsOsamu Watanabe, Takashi Yokomori
PublisherSpringer Verlag
Number of pages13
ISBN (Print)3540667482, 9783540667483
StatePublished - 1999
Externally publishedYes
Event10th International Conference on Algorithmic Learning Theory, ALT 1999 - Tokyo, Japan
Duration: Dec 6 1999Dec 8 1999

Publication series

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


Conference10th International Conference on Algorithmic Learning Theory, ALT 1999

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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