%0 Book Section %1 Freund1995adaboost %A Freund, Yoav %A Schapire, Robert E. %B Computational Learning Theory: Second European Conference, EuroCOLT '95 Barcelona, Spain, March 13--15, 1995 Proceedings %C Berlin, Heidelberg %D 1995 %I Springer Berlin Heidelberg %K lursurvey %P 23--37 %R 10.1007/3-540-59119-2_166 %T A desicion-theoretic generalization of on-line learning and an application to boosting %U https://doi.org/10.1007/3-540-59119-2_166 %X We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update rule of Littlestone and Warmuth 10 can be adapted to this mode yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in ℝ n . We also show how the weight-update rule can be used to derive a new boosting algorithm which does not require prior knowledge about the performance of the weak learning algorithm. %@ 978-3-540-49195-8

@inbook{Freund1995adaboost, abstract = {We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update rule of Littlestone and Warmuth [10] can be adapted to this mode yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in ℝ n . We also show how the weight-update rule can be used to derive a new boosting algorithm which does not require prior knowledge about the performance of the weak learning algorithm.}, added-at = {2017-12-30T18:34:14.000+0100}, address = {Berlin, Heidelberg}, author = {Freund, Yoav and Schapire, Robert E.}, biburl = {https://www.bibsonomy.org/bibtex/239dc487a98c90caa6e46ebcabe4f1732/lautenschlager}, booktitle = {Computational Learning Theory: Second European Conference, EuroCOLT '95 Barcelona, Spain, March 13--15, 1995 Proceedings}, description = {A desicion-theoretic generalization of on-line learning and an application to boosting | SpringerLink}, doi = {10.1007/3-540-59119-2_166}, interhash = {980905f284b39dedbb5ff6299276f076}, intrahash = {39dc487a98c90caa6e46ebcabe4f1732}, isbn = {978-3-540-49195-8}, keywords = {lursurvey}, pages = {23--37}, publisher = {Springer Berlin Heidelberg}, timestamp = {2018-01-19T12:16:18.000+0100}, title = {A desicion-theoretic generalization of on-line learning and an application to boosting}, url = {https://doi.org/10.1007/3-540-59119-2_166}, year = 1995 }