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Robert Schapire
Robert Schapire
Microsoft Research
Bestätigte E-Mail-Adresse bei microsoft.com
Titel
Zitiert von
Zitiert von
Jahr
A decision-theoretic generalization of on-line learning and an application to boosting
Y Freund, RE Schapire
Journal of computer and system sciences 55 (1), 119-139, 1997
278051997
Maximum entropy modeling of species geographic distributions
SJ Phillips, RP Anderson, RE Schapire
Ecological modelling 190 (3-4), 231-259, 2006
189712006
Experiments with a new boosting algorithm
Y Freund, RE Schapire
icml 96, 148-156, 1996
127341996
Novel methods improve prediction of species’ distributions from occurrence data
J Elith*, C H. Graham*, R P. Anderson, M Dudík, S Ferrier, A Guisan, ...
Ecography 29 (2), 129-151, 2006
97862006
The strength of weak learnability
RE Schapire
Machine learning 5, 197-227, 1990
70271990
A short introduction to boosting
Y Freund, R Schapire, N Abe
Journal-Japanese Society For Artificial Intelligence 14 (771-780), 1612, 1999
51251999
Improved boosting algorithms using confidence-rated predictions
RE Schapire, Y Singer
Proceedings of the eleventh annual conference on Computational learning …, 1998
48621998
Boosting the margin: A new explanation for the effectiveness of voting methods
P Bartlett, Y Freund, WS Lee, RE Schapire
The annals of statistics 26 (5), 1651-1686, 1998
38821998
BoosTexter: A boosting-based system for text categorization
RE Schapire, Y Singer
Machine learning 39, 135-168, 2000
31992000
A contextual-bandit approach to personalized news article recommendation
L Li, W Chu, J Langford, RE Schapire
Proceedings of the 19th international conference on World wide web, 661-670, 2010
31982010
A maximum entropy approach to species distribution modeling
SJ Phillips, M Dudík, RE Schapire
Proceedings of the twenty-first international conference on Machine learning, 83, 2004
31262004
The nonstochastic multiarmed bandit problem
P Auer, N Cesa-Bianchi, Y Freund, RE Schapire
SIAM journal on computing 32 (1), 48-77, 2002
29692002
The boosting approach to machine learning: An overview
RE Schapire
Nonlinear estimation and classification, 149-171, 2003
29502003
An efficient boosting algorithm for combining preferences
Y Freund, R Iyer, RE Schapire, Y Singer
Journal of machine learning research 4 (Nov), 933-969, 2003
28842003
Reducing multiclass to binary: A unifying approach for margin classifiers
EL Allwein, RE Schapire, Y Singer
Journal of machine learning research 1 (Dec), 113-141, 2000
26702000
Opening the black box: An open‐source release of Maxent
SJ Phillips, RP Anderson, M Dudík, RE Schapire, ME Blair
Ecography 40 (7), 887-893, 2017
20602017
A brief introduction to boosting
RE Schapire
Ijcai 99 (999), 1401-1406, 1999
19611999
Large margin classification using the perceptron algorithm
Y Freund, RE Schapire
Proceedings of the eleventh annual conference on Computational learning …, 1998
19581998
Boosting: Foundations and algorithms
RE Schapire, Y Freund
Kybernetes 42 (1), 164-166, 2013
14992013
Explaining adaboost
RE Schapire
Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, 37-52, 2013
13372013
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