. We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely...
%0 Journal Article
%1 citeulike:411635
%A Schapire, Robert E.
%A Singer, Yoram
%D 1999
%J Machine Learning
%K adaboost, adaboostmh, adaboostmr, boostexter, boosting, confidence
%N 3
%P 297--336
%T Improved Boosting Using Confidence-rated Predictions
%U http://citeseer.ist.psu.edu/17487.html
%V 37
%X . We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely...
@article{citeulike:411635,
abstract = {. We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely...},
added-at = {2008-06-17T16:01:02.000+0200},
author = {Schapire, Robert E. and Singer, Yoram},
biburl = {https://www.bibsonomy.org/bibtex/219f7f1eea8838f83095f24bfc7a8ac35/pprett},
citeulike-article-id = {411635},
comment = {(private-note)Introduces AdaBoost.MH and AdaBoost.MR - specifically used for multiclass problems. Companion paper to "BoosTexter".},
interhash = {6c5a13f0f0194b3a1c1e96a7f46a26f1},
intrahash = {19f7f1eea8838f83095f24bfc7a8ac35},
journal = {Machine Learning},
keywords = {adaboost, adaboostmh, adaboostmr, boostexter, boosting, confidence},
number = 3,
pages = {297--336},
posted-at = {2008-03-10 20:07:33},
priority = {2},
timestamp = {2008-06-17T16:01:20.000+0200},
title = {Improved Boosting Using Confidence-rated Predictions},
url = {http://citeseer.ist.psu.edu/17487.html},
volume = 37,
year = 1999
}