The Boosting Approach to Machine Learning: An Overview

  • Robert E. Schapire
Part of the Lecture Notes in Statistics book series (LNS, volume 171)

Summary

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Robert E. Schapire
    • 1
  1. 1.AT&T Labs — Research, Shannon LaboratoryFlorham ParkUSA

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