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Thomas Dietterich
Thomas Dietterich
Distinguished Professor (Emeritus), Computer Science, Oregon State University
Bestätigte E-Mail-Adresse bei cs.orst.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Ensemble methods in machine learning
TG Dietterich
International workshop on multiple classifier systems, 1-15, 2000
113932000
Approximate statistical tests for comparing supervised classification learning algorithms
TG Dietterich
Neural computation 10 (7), 1895-1923, 1998
47491998
Benchmarking neural network robustness to common corruptions and perturbations
D Hendrycks, T Dietterich
arXiv preprint arXiv:1903.12261, 2019
40392019
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
TG Dietterich
Machine learning 40, 139-157, 2000
39922000
Solving multiclass learning problems via error-correcting output codes
TG Dietterich, G Bakiri
Journal of artificial intelligence research 2, 263-286, 1994
39691994
Solving the multiple instance problem with axis-parallel rectangles
TG Dietterich, RH Lathrop, T Lozano-Pérez
Artificial intelligence 89 (1-2), 31-71, 1997
36661997
Hierarchical reinforcement learning with the MAXQ value function decomposition
TG Dietterich
Journal of artificial intelligence research 13, 227-303, 2000
22412000
Machine-learning research: Four Current Directions
TG Dietterich
AI magazine 18 (4), 97, 1997
21781997
Deep anomaly detection with outlier exposure
D Hendrycks, M Mazeika, T Dietterich
arXiv preprint arXiv:1812.04606, 2018
18352018
Ensemble learning
TG Dietterich
The handbook of brain theory and neural networks 2 (1), 110-125, 2002
13382002
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
11562014
Overfitting and undercomputing in machine learning
T Dietterich
ACM computing surveys (CSUR) 27 (3), 326-327, 1995
11421995
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
11262021
Learning with many irrelevant features
H Almuallim, TG Dietterich
Oregon State University, 1991
10861991
Machine learning for sequential data: A review
TG Dietterich
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2002
10222002
Pruning adaptive boosting
DD Margineantu, TG Dietterich
ICML 97, 211-218, 1997
8461997
Understanding the psychology behind physician attitudes, behaviors, and engagement as the pathway to physician well-being
AH Rosenstein
Journal of Psychology & Clinical Psychiatry 5 (6), 1-3, 2016
7662016
Learning boolean concepts in the presence of many irrelevant features
H Almuallim, TG Dietterich
Artificial intelligence 69 (1-2), 279-305, 1994
7201994
A reinforcement learning approach to job-shop scheduling
W Zhang, TG Dietterich
Ijcai 95, 1114-1120, 1995
6471995
Readings in machine learning
J Shavlik, T Dietterich
Morgan Kaufmann Publishers., 1990
6381990
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