Bayes-risk Consistency of Boosting
The probability of error of classification methods based on convex combinations of simple base classifiers by “boosting” algorithms is investigated. We show in this talk that certain regularized boosting algorithms provide Bayes-risk consistent classifiers under the only assumption that the Bayes classifier may be approximated by a convex combination of the base classifiers. Non-asymptotic distribution-free bounds are also developed which offer interesting new insight into how boosting works and help explain their success in practical classification problems.