Robert Schapire
Robert Schapire
Microsoft Research
Verified email at microsoft.com
Title
Cited by
Cited by
Year
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
216021997
Maximum entropy modeling of species geographic distributions
SJ Phillips, RP Anderson, RE Schapire
Ecological modelling 190 (3-4), 231-259, 2006
142892006
Experiments with a new boosting algorithm
Y Freund, RE Schapire
icml 96, 148-156, 1996
108321996
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
78702006
The strength of weak learnability
RE Schapire
Machine learning 5 (2), 197-227, 1990
59201990
Improved boosting algorithms using confidence-rated predictions
RE Schapire, Y Singer
Machine learning 37 (3), 297-336, 1999
45421999
A short introduction to boosting
Y Freund, R Schapire, N Abe
Journal-Japanese Society For Artificial Intelligence 14 (771-780), 1612, 1999
41731999
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
35521998
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel
Proceedings of the 30th International Conference on Neural Information …, 2016
31012016
BoosTexter: A boosting-based system for text categorization
RE Schapire, Y Singer
Machine learning 39 (2), 135-168, 2000
29542000
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
26722003
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
24472004
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
24342000
The boosting approach to machine learning: An overview
RE Schapire
Nonlinear estimation and classification, 149-171, 2003
24002003
The nonstochastic multiarmed bandit problem
P Auer, N Cesa-Bianchi, Y Freund, RE Schapire
SIAM journal on computing 32 (1), 48-77, 2002
22012002
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
20952010
Large margin classification using the perceptron algorithm
Y Freund, RE Schapire
Machine learning 37 (3), 277-296, 1999
17431999
A brief introduction to boosting
RE Schapire
Ijcai 99, 1401-1406, 1999
16281999
Boosting: Foundations and algorithms
RE Schapire, Y Freund
Kybernetes, 2013
9482013
Gambling in a rigged casino: The adversarial multi-armed bandit problem
P Auer, N Cesa-Bianchi, Y Freund, RE Schapire
Proceedings of IEEE 36th Annual Foundations of Computer Science, 322-331, 1995
9091995
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