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 | 29564 | 1997 |
Maximum entropy modeling of species geographic distributions SJ Phillips, RP Anderson, RE Schapire Ecological modelling 190 (3-4), 231-259, 2006 | 20343 | 2006 |
Experiments with a new boosting algorithm Y Freund, RE Schapire icml 96, 148-156, 1996 | 13216 | 1996 |
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 | 10295 | 2006 |
The strength of weak learnability RE Schapire Machine learning 5, 197-227, 1990 | 7343 | 1990 |
A short introduction to boosting Y Freund, R Schapire, N Abe Journal-Japanese Society For Artificial Intelligence 14 (771-780), 1612, 1999 | 5388 | 1999 |
Improved boosting algorithms using confidence-rated predictions RE Schapire, Y Singer Proceedings of the eleventh annual conference on Computational learning …, 1998 | 4937 | 1998 |
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 | 3968 | 1998 |
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 | 3548 | 2010 |
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 | 3297 | 2004 |
BoosTexter: A boosting-based system for text categorization RE Schapire, Y Singer Machine learning 39, 135-168, 2000 | 3196 | 2000 |
The nonstochastic multiarmed bandit problem P Auer, N Cesa-Bianchi, Y Freund, RE Schapire SIAM journal on computing 32 (1), 48-77, 2002 | 3179 | 2002 |
The boosting approach to machine learning: An overview RE Schapire Nonlinear estimation and classification, 149-171, 2003 | 3109 | 2003 |
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 | 2948 | 2003 |
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 | 2724 | 2000 |
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 | 2462 | 2017 |
A brief introduction to boosting RE Schapire Ijcai 99 (999), 1401-1406, 1999 | 2079 | 1999 |
Large margin classification using the perceptron algorithm Y Freund, RE Schapire Proceedings of the eleventh annual conference on Computational learning …, 1998 | 2002 | 1998 |
Explaining adaboost RE Schapire Empirical inference: festschrift in honor of vladimir N. Vapnik, 37-52, 2013 | 1622 | 2013 |
Boosting: Foundations and algorithms RE Schapire, Y Freund Kybernetes 42 (1), 164-166, 2013 | 1559 | 2013 |