Description: 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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boosting-survey.pdf
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