@inproceedings{6d23760ba30140158d55c10575620371,
title = "Theoretical views of boosting",
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. We also briefly mention some empirical work.",
author = "Schapire, {Robert E.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.; 4th European Conference on Computational Learning Theory, EuroCOLT 1999 ; Conference date: 29-03-1999 Through 31-03-1999",
year = "1999",
doi = "10.1007/3-540-49097-3_1",
language = "English (US)",
isbn = "3540657010",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--10",
editor = "Paul Fischer and Simon, {Hans Ulrich}",
booktitle = "Computational Learning Theory - 4th European Conference, EuroCOLT 1999, Proceedings",
address = "Germany",
}