The probability of error of classification methods based on convex combinations of simple base classifiers by "boosting" algorithms is investigated. The main result of the paper is 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. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Lugosi, G., & Vayatis, N. (2002). A consistent strategy for boosting algorithms. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2375, 303–319. https://doi.org/10.1007/3-540-45435-7_21
Mendeley helps you to discover research relevant for your work.