Thomas Dietterich 个人学术档案

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	总计	2019 年至今
引用	64469	26195
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Prasad TadepalliProfessor of Computer Science, Oregon State University在 eecs.oregonstate.edu 的电子邮件经过验证
Jon HerlockerCEO Tignis, inc在 herlocker.org 的电子邮件经过验证
Dan HendrycksDirector of the Center for AI Safety在 berkeley.edu 的电子邮件经过验证
David G. UllmanDavid Ullman LLC.在 davidullman.com 的电子邮件经过验证
Linda ShapiroProfessor of Computer Science & Engineering, University of Washington在 cs.washington.edu 的电子邮件经过验证
Richard H. LathropProfessor of Computer Science, University of California Irvine在 uci.edu 的电子邮件经过验证
Hussein AlmuallimPresident, FirmSoft Technologies在 firmsofttech.com 的电子邮件经过验证
Weng-Keen WongProfessor of Computer Science, Oregon State University在 eecs.oregonstate.edu 的电子邮件经过验证
Tomas Lozano-PerezProfessor of Computer Science, MIT在 mit.edu 的电子邮件经过验证
Nicholas S. FlannAssociate Professor of Computer Science, Utah State University在 usu.edu 的电子邮件经过验证
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of Wisconsin在 cs.wisc.edu 的电子邮件经过验证
David A. LytleOregon State University在 oregonstate.edu 的电子邮件经过验证
Simone StumpfUniversity of Glasgow在 glasgow.ac.uk 的电子邮件经过验证
Dragos D MargineantuBoeing Research & Technology在 boeing.com 的电子邮件经过验证
Jianqiang ShenPalo Alto Research Center (PARC)在 parc.com 的电子邮件经过验证
Xiaoli FernOregon State University在 eecs.oregonstate.edu 的电子邮件经过验证
Janardhan Rao (Jana) DoppaHuie-Rogers Endowed Chair Associate Professor, Washington State University在 eecs.wsu.edu 的电子邮件经过验证
Hongli Dengprincipal applied scientist在 amazon.com 的电子邮件经过验证
Claire MontgomeryEmeritus Professor, Oregon State University, College of Forestry在 oregonstate.edu 的电子邮件经过验证
Daniel SheldonUniversity of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证

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Thomas Dietterich

Distinguished Professor (Emeritus), Computer Science, Oregon State University

在 cs.orst.edu 的电子邮件经过验证 - 首页

Machine Learning Computational Sustainability Artificial Intelligence Reinforcement Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Ensemble methods in machine learning TG Dietterich International workshop on multiple classifier systems, 1-15, 2000	10543	2000
Approximate statistical tests for comparing supervised classification learning algorithms TG Dietterich Neural computation 10 (7), 1895-1923, 1998	4536	1998
Solving multiclass learning problems via error-correcting output codes TG Dietterich, G Bakiri Journal of artificial intelligence research 2, 263-286, 1994	3906	1994
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization TG Dietterich Machine learning 40, 139-157, 2000	3858	2000
Solving the multiple instance problem with axis-parallel rectangles TG Dietterich, RH Lathrop, T Lozano-Pérez Artificial intelligence 89 (1-2), 31-71, 1997	3498	1997
Benchmarking neural network robustness to common corruptions and perturbations D Hendrycks, T Dietterich arXiv preprint arXiv:1903.12261, 2019	3290	2019
Machine-learning research: Four Current Directions TG Dietterich AI magazine 18 (4), 97, 1997	2148	1997
Hierarchical reinforcement learning with the MAXQ value function decomposition TG Dietterich Journal of artificial intelligence research 13, 227-303, 2000	2118	2000
Deep anomaly detection with outlier exposure D Hendrycks, M Mazeika, T Dietterich arXiv preprint arXiv:1812.04606, 2018	1542	2018
Ensemble learning TG Dietterich The handbook of brain theory and neural networks 2 (1), 110-125, 2002	1249*	2002
Learning with many irrelevant features H Almuallim, TG Dietterich Oregon State University, 1991	1080	1991
The eBird enterprise: An integrated approach to development and application of citizen science BL Sullivan, JL Aycrigg, JH Barry, RE Bonney, N Bruns, CB Cooper, ... Biological conservation 169, 31-40, 2014	1016	2014
Overfitting and undercomputing in machine learning T Dietterich ACM computing surveys (CSUR) 27 (3), 326-327, 1995	1004	1995
Machine learning for sequential data: A review TG Dietterich Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2002	981	2002
A unifying review of deep and shallow anomaly detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE 109 (5), 756-795, 2021	850	2021
Pruning adaptive boosting DD Margineantu, TG Dietterich ICML 97, 211-218, 1997	803	1997
To transfer or not to transfer MT Rosenstein, Z Marx, LP Kaelbling, TG Dietterich NIPS 2005 workshop on transfer learning 898 (3), 4, 2005	717	2005
Learning boolean concepts in the presence of many irrelevant features H Almuallim, TG Dietterich Artificial intelligence 69 (1-2), 279-305, 1994	712	1994
A reinforcement learning approach to job-shop scheduling W Zhang, TG Dietterich Ijcai 95, 1114-1120, 1995	625	1995
Readings in machine learning J Shavlik, T Dietterich Morgan Kaufmann Publishers., 1990	624	1990

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