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AdaBoost

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Computer Vision

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Synonyms

Adaptive boosting; Discrete AdaBoost

Related Concepts

Definition

The AdaBoost algorithm learns a classifier from data by combining additively a number of weak classifiers. The weak classifiers are incorporated sequentially, one at a time, in order to reduce the empirical exponential classification risk of the combined classifier.

Background

Boosting [1, 2], introduced by Robert Schapire in [3], is a general technique for combining the response of several predictors with limited accuracy into a single, more accurate prediction. AdaBoost is a popular implementation of boosting for binary classification [4]. Soon after its introduction, AdaBoost became one of the most popular learning...

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References

  1. Friedman JH, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–374

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  3. Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197–227

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  4. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning, Bari, pp 148–156

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  5. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

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  9. Schapire RE, Singer Y (1998) Improved boosting algorithms using confidence-rated predictions. In: Computational learning theory. Springer, New York, pp 80–91

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bern, Bern, Switzerland

    Paolo Favaro

  2. Engineering Science Department, Oxford University, Oxford, UK

    Andrea Vedaldi

Authors
  1. Paolo Favaro
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  2. Andrea Vedaldi
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Corresponding author

Correspondence to Paolo Favaro .

Section Editor information

  1. No affiliation provided

    Daniel Cremers

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Favaro, P., Vedaldi, A. (2021). AdaBoost. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_663-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_663-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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