Chapter

Nonlinear Estimation and Classification

Volume 171 of the series Lecture Notes in Statistics pp 149-171

The Boosting Approach to Machine Learning: An Overview

  • Robert E. SchapireAffiliated withAT&T Labs — Research, Shannon Laboratory

* Final gross prices may vary according to local VAT.

Get Access

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.