Thomas Dietterich

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Prasad TadepalliProfessor of Computer Science, Oregon State Universityeecs.oregonstate.edu의 이메일 확인됨
Dan HendrycksDirector of the Center for AI Safety (advisor for xAI and Scale)berkeley.edu의 이메일 확인됨
Jon HerlockerCEO Tignis, incherlocker.org의 이메일 확인됨
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Linda ShapiroProfessor of Computer Science & Engineering, University of Washingtoncs.washington.edu의 이메일 확인됨
Richard H. LathropProfessor of Computer Science, University of California Irvineuci.edu의 이메일 확인됨
Weng-Keen WongProfessor of Computer Science, Oregon State Universityeecs.oregonstate.edu의 이메일 확인됨
Hussein AlmuallimPresident, FirmSoft Technologiesfirmsofttech.com의 이메일 확인됨
Tomas Lozano-PerezProfessor of Computer Science, MITmit.edu의 이메일 확인됨
Nicholas S. FlannAssociate Professor of Computer Science, Utah State Universityusu.edu의 이메일 확인됨
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of Wisconsincs.wisc.edu의 이메일 확인됨
David A. LytleOregon State Universityoregonstate.edu의 이메일 확인됨
Simone StumpfUniversity of Glasgowglasgow.ac.uk의 이메일 확인됨
Dragos D MargineantuBoeing Research & Technologyboeing.com의 이메일 확인됨
Jianqiang ShenPalo Alto Research Center (PARC)parc.com의 이메일 확인됨
Xiaoli FernOregon State Universityeecs.oregonstate.edu의 이메일 확인됨
Janardhan Rao (Jana) DoppaHuie-Rogers Endowed Chair Associate Professor, Washington State Universityeecs.wsu.edu의 이메일 확인됨
Hongli Dengprincipal applied scientistamazon.com의 이메일 확인됨
Claire MontgomeryEmeritus Professor, Oregon State University, College of Forestryoregonstate.edu의 이메일 확인됨
Daniel SheldonUniversity of Massachusetts Amherstcs.umass.edu의 이메일 확인됨

팔로우

Thomas Dietterich

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

cs.orst.edu의 이메일 확인됨 - 홈페이지

Machine Learning Computational Sustainability Artificial Intelligence Reinforcement Learning


제목 서지정보순 정렬 연도순 정렬 제목순 정렬	인용 인용	연도
Ensemble methods in machine learning TG Dietterich International workshop on multiple classifier systems, 1-15, 2000	11125	2000
Approximate statistical tests for comparing supervised classification learning algorithms TG Dietterich Neural computation 10 (7), 1895-1923, 1998	4694	1998
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization TG Dietterich Machine learning 40, 139-157, 2000	3956	2000
Solving multiclass learning problems via error-correcting output codes TG Dietterich, G Bakiri Journal of artificial intelligence research 2, 263-286, 1994	3946	1994
Benchmarking neural network robustness to common corruptions and perturbations D Hendrycks, T Dietterich arXiv preprint arXiv:1903.12261, 2019	3762	2019
Solving the multiple instance problem with axis-parallel rectangles TG Dietterich, RH Lathrop, T Lozano-Pérez Artificial intelligence 89 (1-2), 31-71, 1997	3596	1997
Hierarchical reinforcement learning with the MAXQ value function decomposition TG Dietterich Journal of artificial intelligence research 13, 227-303, 2000	2185	2000
Machine-learning research: Four Current Directions TG Dietterich AI magazine 18 (4), 97, 1997	2172	1997
Deep anomaly detection with outlier exposure D Hendrycks, M Mazeika, T Dietterich arXiv preprint arXiv:1812.04606, 2018	1710	2018
Ensemble learning TG Dietterich The handbook of brain theory and neural networks 2 (1), 110-125, 2002	1318*	2002
Overfitting and undercomputing in machine learning T Dietterich ACM computing surveys (CSUR) 27 (3), 326-327, 1995	1099	1995
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	1092	2014
Learning with many irrelevant features H Almuallim, TG Dietterich Oregon State University, 1991	1084	1991
Machine learning for sequential data: A review TG Dietterich Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2002	1016	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	1005	2021
Pruning adaptive boosting DD Margineantu, TG Dietterich ICML 97, 211-218, 1997	821	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	750	2005
Learning boolean concepts in the presence of many irrelevant features H Almuallim, TG Dietterich Artificial intelligence 69 (1-2), 279-305, 1994	718	1994
A reinforcement learning approach to job-shop scheduling W Zhang, TG Dietterich Ijcai 95, 1114-1120, 1995	642	1995
Readings in machine learning J Shavlik, T Dietterich Morgan Kaufmann Publishers., 1990	636	1990

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