Boosting is a general metho for improving the accuracy of any given learnign algorithm. Focusing primarily on the adaboost algorithm, we briefly survey theoretical work o 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 ofr multiclass classification problems. We also briefly mention some emprical work.
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