| Year |
Citation |
Score |
| 2020 |
Lauer CJ, Montgomery CA, Dietterich TG. Evaluating wildland fire liability standards - does regulation incentivise good management? International Journal of Wildland Fire. 29: 572-580. DOI: 10.1071/Wf19090 |
0.312 |
|
| 2019 |
Zemicheal T, Dietterich TG. Anomaly detection in the presence of missing values for weather data quality control The Compass. 65-73. DOI: 10.1145/3314344.3332490 |
0.398 |
|
| 2018 |
O'Leary MA, Alphonse K, Mariangeles AH, Cavaliere D, Cirranello A, Dietterich TG, Julius M, Kaufman S, Law E, Passarotti M, Reft A, Robalino J, Simmons NB, Smith SY, Stevenson DW, et al. Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes. Systematic Biology. 67: 49-60. PMID 29253296 DOI: 10.1093/Sysbio/Syx052 |
0.319 |
|
| 2017 |
McGregor S, Buckingham H, Dietterich TG, Houtman R, Montgomery C, Metoyer R. Interactive visualization for testing Markov Decision Processes: MDPVIS Journal of Visual Languages & Computing. 39: 93-106. DOI: 10.1016/J.Jvlc.2016.10.007 |
0.699 |
|
| 2017 |
Lauer CJ, Montgomery CA, Dietterich TG. Spatial interactions and optimal forest management on a fire-threatened landscape Forest Policy and Economics. 83: 107-120. DOI: 10.1016/J.Forpol.2017.07.006 |
0.329 |
|
| 2017 |
Hall KM, Albers HJ, Alkaee Taleghan M, Dietterich TG. Optimal Spatial-Dynamic Management of Stochastic Species Invasions Environmental and Resource Economics. 70: 403-427. DOI: 10.1007/S10640-017-0127-6 |
0.329 |
|
| 2015 |
Russell S, Dietterich T, Horvitz E, Selman B, Rossi F, Hassabis D, Legg S, Suleyman M, George D, Phoenix S. Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter Ai Magazine. 36: 3. DOI: 10.1609/Aimag.V36I4.2621 |
0.316 |
|
| 2015 |
Hutchinson RA, Valente JJ, Emerson SC, Betts MG, Dietterich TG. Penalized likelihood methods improve parameter estimates in occupancy models Methods in Ecology and Evolution. 6: 949-959. DOI: 10.1111/2041-210X.12368 |
0.309 |
|
| 2014 |
Farnsworth A, Sheldon D, Geevarghese J, Irvine J, Van Doren B, Webb K, Dietterich TG, Kelling S. Reconstructing velocities of migrating birds from weather radar - A case study in computational sustainability Ai Magazine. 35: 31-48. DOI: 10.1609/Aimag.V35I2.2527 |
0.363 |
|
| 2014 |
Sullivan BL, Aycrigg JL, Barry JH, Bonney RE, Bruns N, Cooper CB, Damoulas T, Dhondt AA, Dietterich T, Farnsworth A, Fink D, Fitzpatrick JW, Fredericks T, Gerbracht J, Gomes C, et al. The eBird enterprise: An integrated approach to development and application of citizen science Biological Conservation. 169: 31-40. DOI: 10.1016/J.Biocon.2013.11.003 |
0.356 |
|
| 2013 |
Burleigh JG, Alphonse K, Alverson AJ, Bik HM, Blank C, Cirranello AL, Cui H, Daly M, Dietterich TG, Gasparich G, Irvine J, Julius M, Kaufman S, Law E, Liu J, et al. Next-generation phenomics for the Tree of Life. Plos Currents. 5. PMID 23827969 DOI: 10.1371/Currents.Tol.085C713Acafc8711B2Ff7010A4B03733 |
0.319 |
|
| 2013 |
Houtman RM, Montgomery CA, Gagnon AR, Calkin DE, Dietterich TG, McGregor S, Crowley M. Allowing a wildfire to burn: Estimating the effect on future fire suppression costs International Journal of Wildland Fire. 22: 871-882. DOI: 10.1071/Wf12157 |
0.696 |
|
| 2012 |
Zhang XS, Shrestha B, Yoon S, Kambhampati S, Dibona P, Guo JK, McFarlane D, Hofmann MO, Whitebread K, Appling DS, Whitaker ET, Trewhitt EB, Ding L, Michaelis JR, McGuinness DL, ... ... Dietterich TG, et al. An ensemble architecture for learning complex problem-solving techniques from demonstration Acm Transactions On Intelligent Systems and Technology. 3. DOI: 10.1145/2337542.2337560 |
0.321 |
|
| 2011 |
Dereszynski EW, Dietterich TG. Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns Acm Transactions On Sensor Networks. 8. DOI: 10.1145/1993042.1993045 |
0.672 |
|
| 2009 |
Dietterich TG. Machine learning and ecosystem informatics: Challenges and opportunities Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5828: 1-5. DOI: 10.1007/978-3-642-05224-8_1 |
0.332 |
|
| 2009 |
Shen J, Dietterich TG. A family of large margin linear classifiers and its application in dynamic environments Statistical Analysis and Data Mining. 2: 328-345. DOI: 10.1002/Sam.V2:5/6 |
0.355 |
|
| 2009 |
Dietterich TG. Machine learning in ecosystem informatics and sustainability Ijcai International Joint Conference On Artificial Intelligence. 8-13. |
0.351 |
|
| 2008 |
Natarajan S, Tadepalli P, Dietterich TG, Fern A. Learning first-order probabilistic models with combining rules Annals of Mathematics and Artificial Intelligence. 54: 223-256. DOI: 10.1007/S10472-009-9138-5 |
0.35 |
|
| 2007 |
Dereszynski EW, Dietterich TG. Probabilistic models for anomaly detection in remote sensor data streams Proceedings of the 23rd Conference On Uncertainty in Artificial Intelligence, Uai 2007. 75-82. |
0.673 |
|
| 2006 |
Langford WT, Gergel SE, Dietterich TG, Cohen W. Map misclassification can cause large errors in landscape pattern indices: Examples from habitat fragmentation Ecosystems. 9: 474-488. DOI: 10.1007/S10021-005-0119-1 |
0.706 |
|
| 2005 |
Bayer-Zubek V, Dietterich TG. Integrating learning from examples into the search for diagnostic policies Journal of Artificial Intelligence Research. 24: 263-303. DOI: 10.1613/Jair.1512 |
0.323 |
|
| 2002 |
Dietterich TG. Machine learning for sequential data: A review Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2396: 15-30. DOI: 10.1007/3-540-70659-3_2 |
0.345 |
|
| 2000 |
Dietterich TG. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition Journal of Artificial Intelligence Research. 13: 227-303. DOI: 10.1613/Jair.639 |
0.317 |
|
| 2000 |
Dietterich TG. Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization Machine Learning. 40: 139-157. DOI: 10.1023/A:1007607513941 |
0.323 |
|
| 1997 |
Dietterich TG. Machine-learning research: Four current directions Ai Magazine. 18: 97-136. DOI: 10.1609/Aimag.V18I4.1324 |
0.317 |
|
| 1997 |
Dietterich TG, Flann NS. Explanation-Based Learning and Reinforcement Learning: A Unified View Machine Learning. 28: 169-210. DOI: 10.1023/A:1007355226281 |
0.316 |
|
| 1995 |
Dietterich TG, Bakiri G. Solving Multiclass Learning Problems via Error-Correcting Output Codes Journal of Artificial Intelligence Research. 2: 263-286. DOI: 10.1613/Jair.105 |
0.348 |
|
| 1995 |
Dietterich TG, Hild H, Bakiri G. A Comparison of ID3 and Backpropagation for English Text-To-Speech Mapping Machine Learning. 18: 51-80. DOI: 10.1023/A:1022822623726 |
0.31 |
|
| 1994 |
Jain AN, Dietterich TG, Lathrop RH, Chapman D, Critchlow RE, Bauer BE, Webster TA, Lozano-Perez T. Compass: A shape-based machine learning tool for drug design Journal of Computer-Aided Molecular Design. 8: 635-652. PMID 7738601 DOI: 10.1007/Bf00124012 |
0.307 |
|
| 1989 |
Flann NS, Dietterich TG. A Study of Explanation-Based Methods for Inductive Learning Machine Learning. 4: 187-226. DOI: 10.1023/A:1022652016863 |
0.307 |
|
| 1981 |
Dietterich TG, Michalski RS. Inductive learning of structural descriptions. Evaluation criteria and comparative review of selected methods Artificial Intelligence. 16: 257-294. DOI: 10.1016/0004-3702(81)90002-3 |
0.327 |
|
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