@inproceedings{d4930e65406849b8bbf3467fcfb1097f,
title = "Theoretical, views of boosting and applications",
abstract = "Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, we briefly survey theoretical work on boosting including analyses of AdaBoost{\textquoteright}s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. Some empirical work and applications are also described.",
author = "Schapire, {Robert E.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.; 10th International Conference on Algorithmic Learning Theory, ALT 1999 ; Conference date: 06-12-1999 Through 08-12-1999",
year = "1999",
doi = "10.1007/3-540-46769-6_2",
language = "English (US)",
isbn = "3540667482",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "13--25",
editor = "Osamu Watanabe and Takashi Yokomori",
booktitle = "Algorithmic Learning Theory - 10th International Conference, ALT 1999, Proceedings",
address = "Germany",
}