Thomas G. Dietterich

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2011
118Incorporating Boosted Regression Trees into Ecological Latent Variable Models. Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich. AAAI 2011. Web SearchBibTeXDownload
117FolderPredictor: Reducing the cost of reaching the right folder. Xinlong Bao, Thomas G. Dietterich. ACM TIST (2): 8 (2011). Web SearchBibTeXDownload
116Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning. Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich. AI Magazine (32): 35-50 (2011). Web SearchBibTeXDownload
115Integrating Learning from Examples into the Search for Diagnostic Policies. Valentina Bayer Zubek, Thomas G. Dietterich. CoRR (abs/1109.2127) (2011). Web SearchBibTeXDownload
114Learning Rules from Incomplete Examples via Implicit Mention Models. Janardhan Rao Doppa, Shahed Sorower, Mohammad NasrEsfahani, Walker Orr, Thomas G. Dietterich, Xiaoli Fern, Prasad Tadepalli, Jed Irvine. Journal of Machine Learning Research - Proceedings Track (20): 197-212 (2011). Web SearchBibTeXDownload
113Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns. Ethan W. Dereszynski, Thomas G. Dietterich. TOSN (8): 3 (2011). Web SearchBibTeXDownload
2010
112Reinforcement Learning Via Practice and Critique Advice. Kshitij Judah, Saikat Roy, Alan Fern, Thomas G. Dietterich. AAAI 2010. Web SearchBibTeXDownload
111The life and times of files and information: a study of desktop provenance. Carlos Jensen, Heather Lonsdale, Eleanor Wynn, Jill Cao, Michael Slater, Thomas G. Dietterich. CHI 2010, 767-776. Web SearchBibTeXDownload
110Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification. Natalia Larios, B. Soran, Linda G. Shapiro, Gonzalo Martínez-Muñoz, J. Lin, Thomas G. Dietterich. ICPR 2010, 2624-2627. Web SearchBibTeXDownload
2009
109Machine Learning and Ecosystem Informatics: Challenges and Opportunities. Thomas G. Dietterich. ACML 2009, 1-5. Web SearchBibTeXDownload
108Dictionary-free categorization of very similar objects via stacked evidence trees. Gonzalo Martínez-Muñoz, Natalia Larios Delgado, Eric N. Mortensen, Wei Zhang, Asako Yamamuro, Robert Paasch, Nadia Payet, David A. Lytle, Linda G. Shapiro, Sinisa Todorovic, Andrew Moldenke, Thomas G. Dietterich. CVPR 2009, 549-556. Web SearchBibTeXDownload
107An Ensemble Learning and Problem Solving Architecture for Airspace Management. Xiaoqin Zhang, Sung Wook Yoon, Phillip DiBona, Darren Appling, Li Ding, Janardhan Rao Doppa, Derek T. Green, Jinhong Guo, Ugur Kuter, Geoffrey Levine, Reid MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontañón, Charles Parker, Jainarayan Radhakrishnan, Anton Rebguns, Bhavesh Shrestha, Zhexuan Song, Ethan Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel D. Corkill, Gerald DeJong, Thomas G. Dietterich, Subbarao Kambhampati, Victor R. Lesser, Deborah L. McGuinness, Ashwin Ram, Diana F. Spears, Prasad Tadepalli, Elizabeth Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, Kenneth R. Whitebread. IAAI 2009. Web SearchBibTeXDownload
106Learning non-redundant codebooks for classifying complex objects. Wei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich. ICML 2009, 156. Web SearchBibTeXDownload
105Machine Learning in Ecosystem Informatics and Sustainability. Thomas G. Dietterich. IJCAI 2009, 8-13. Web SearchBibTeXDownload
104Interacting meaningfully with machine learning systems: Three experiments. Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret M. Burnett, Thomas G. Dietterich, Erin Sullivan, Jonathan L. Herlocker. Int. J. Hum.-Comput. Stud. (67): 639-662 (2009). Web SearchBibTeXDownload
103Discovering frequent work procedures from resource connections. Jianqiang Shen, Erin Fitzhenry, Thomas G. Dietterich. IUI 2009, 277-286. Web SearchBibTeXDownload
102Detecting and correcting user activity switches: algorithms and interfaces. Jianqiang Shen, Jed Irvine, Xinlong Bao, Michael Goodman, Stephen Kolibaba, Anh Tran, Fredric Carl, Brenton Kirschner, Simone Stumpf, Thomas G. Dietterich. IUI 2009, 117-126. Web SearchBibTeXDownload
101A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. Jianqiang Shen, Thomas G. Dietterich. SDM 2009, 164-172. Web SearchBibTeXDownload
100A family of large margin linear classifiers and its application in dynamic environments. Jianqiang Shen, Thomas G. Dietterich. Statistical Analysis and Data Mining (2): 328-345 (2009). Web SearchBibTeXDownload
2008
99Integrating Multiple Learning Components through Markov Logic. Thomas G. Dietterich, Xinlong Bao. AAAI 2008, 622-627. Web SearchBibTeX
98Learning first-order probabilistic models with combining rules. Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern, Alan Fern, Angelo C. Restificar. Ann. Math. Artif. Intell. (54): 223-256 (2008). Web SearchBibTeXDownload
97Learning MDP Action Models Via Discrete Mixture Trees. Michael Wynkoop, Thomas G. Dietterich. ECML/PKDD (2) 2008, 597-612. Web SearchBibTeXDownload
96Automatic discovery and transfer of MAXQ hierarchies. Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich. ICML 2008, 648-655. Web SearchBibTeXDownload
95Learning visual dictionaries and decision lists for object recognition. Wei Zhang, Thomas G. Dietterich. ICPR 2008, 1-4. Web SearchBibTeXDownload
94Structured machine learning: the next ten years. Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli. Machine Learning (73): 3-23 (2008). Cited by 8Web SearchBibTeXDownload
93Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects. Natalia Larios, Hongli Deng, Wei Zhang, Matt Sarpola, Jenny Yuen, Robert Paasch, Andrew Moldenke, David A. Lytle, Salvador Ruiz-Correa, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich. Mach. Vis. Appl. (19): 105-123 (2008). Web SearchBibTeXDownload
2007
92AAAI-07 Workshop Reports. Sarabjot Singh Anand, Daniel Bahls, Catherina Burghart, Mark H. Burstein, Huajun Chen, John Collins, Thomas G. Dietterich, Jon Doyle, Chris Drummond, William Elazmeh, Christopher W. Geib, Judy Goldsmith, Hans W. Guesgen, Jim Hendler, Dietmar Jannach, Nathalie Japkowicz, Ulrich Junker, Gal A. Kaminka, Alfred Kobsa, Jérôme Lang, David B. Leake, Lundy Lewis, Gerard Ligozat, Sofus A. Macskassy, Drew V. McDermott, Ted Metzler, Bamshad Mobasher, Ullas Nambiar, Zaiqing Nie, Klas Orsvärn, Barry O'Sullivan, David V. Pynadath, Jochen Renz, Rita V. Rodríguez, Thomas Roth-Berghofer, Stefan Schulz, Rudi Studer, Yimin Wang, Michael P. Wellman. AI Magazine (28): 119-128 (2007). Web SearchBibTeXDownload
91Machine Learning in Ecosystem Informatics. Thomas G. Dietterich. ALT 2007, 10-11. Web SearchBibTeXDownload
90Principal Curvature-Based Region Detector for Object Recognition. Hongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro. CVPR 2007. Web SearchBibTeXDownload
89Real-Time Detection of Task Switches of Desktop Users. Jianqiang Shen, Lida Li, Thomas G. Dietterich. IJCAI 2007, 2868-2873. Web SearchBibTeXDownload
88Improving Intelligent Assistants for Desktop Activities. Simone Stumpf, Margaret M. Burnett, Thomas G. Dietterich. Interaction Challenges for Intelligent Assistants 2007, 119-121. Web SearchBibTeX
87Toward harnessing user feedback for machine learning. Simone Stumpf, Vidya Rajaram, Lida Li, Margaret M. Burnett, Thomas G. Dietterich, Erin Sullivan, Russell Drummond, Jonathan L. Herlocker. IUI 2007, 82-91. Cited by 21Web SearchBibTeXDownload
86Active EM to reduce noise in activity recognition. Jianqiang Shen, Thomas G. Dietterich. IUI 2007, 132-140. Web SearchBibTeXDownload
8507161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen Muggleton. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007. Web SearchBibTeXDownload
84Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. Ethan W. Dereszynski, Thomas G. Dietterich. UAI 2007, 75-82. Web SearchBibTeXDownload
83Automated Insect Identification through Concatenated Histograms of Local Appearance Features. Natalia Larios, Hongli Deng, Wei Zhang, Matt Sarpola, Jenny Yuen, Robert Paasch, Andrew Moldenke, David A. Lytle, Ruiz Correa, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich. WACV 2007, 26. Web SearchBibTeXDownload
2006
82A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions. Wei Zhang, Hongli Deng, Thomas G. Dietterich, Eric N. Mortensen. ICPR (1) 2006, 778-782. Web SearchBibTeXDownload
81A hybrid learning system for recognizing user tasks from desktop activities and email messages. Jianqiang Shen, Lida Li, Thomas G. Dietterich, Jonathan L. Herlocker. IUI 2006, 86-92. Web SearchBibTeXDownload
80Fewer clicks and less frustration: reducing the cost of reaching the right folder. Xinlong Bao, Jonathan L. Herlocker, Thomas G. Dietterich. IUI 2006, 178-185. Web SearchBibTeXDownload
2005
79The TaskTracker System. Simone Stumpf, Xinlong Bao, Anton N. Dragunov, Thomas G. Dietterich, Jonathan L. Herlocker, Kevin Johnsrude, Lida Li, Jianqiang Shen. AAAI 2005, 1712-1713. Web SearchBibTeX
78Learning first-order probabilistic models with combining rules. Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern, Alan Fern, Angelo C. Restificar. ICML 2005, 609-616. Web SearchBibTeXDownload
77TaskTracer: a desktop environment to support multi-tasking knowledge workers. Anton N. Dragunov, Thomas G. Dietterich, Kevin Johnsrude, Matthew R. McLaughlin, Lida Li, Jonathan L. Herlocker. IUI 2005, 75-82. Web SearchBibTeXDownload
76Integrating Learning from Examples into the Search for Diagnostic Policies. Valentina Bayer Zubek, Thomas G. Dietterich. J. Artif. Intell. Res. (JAIR) (24): 263-303 (2005). Web SearchBibTeXDownload
7505051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton. Probabilistic, Logical and Relational Learning 2005. Cited by 3Web SearchBibTeXDownload
7405051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton. Probabilistic, Logical and Relational Learning 2005. Web SearchBibTeXDownload
73Learning from Sparse Data by Exploiting Monotonicity Constraints. Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich. UAI 2005, 18-26. Web SearchBibTeXDownload
2004
72Improving SVM accuracy by training on auxiliary data sources. Pengcheng Wu, Thomas G. Dietterich. ICML 2004. Web SearchBibTeXDownload
71Training conditional random fields via gradient tree boosting. Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov. ICML 2004. Web SearchBibTeXDownload
70Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods. Giorgio Valentini, Thomas G. Dietterich. Journal of Machine Learning Research (5): 725-775 (2004). Web SearchBibTeXDownload
2003
69Model-based Policy Gradient Reinforcement Learning. Xin Wang, Thomas G. Dietterich. ICML 2003, 776-783. Web SearchBibTeX
68Low Bias Bagged Support Vector Machines. Giorgio Valentini, Thomas G. Dietterich. ICML 2003, 752-759. Web SearchBibTeX
2002
67A Multi-agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation. Dídac Busquets, Ramon López de Mántaras, Carles Sierra, Thomas G. Dietterich. CCIA 2002, 269-281. Web SearchBibTeXDownload
66Pruning Improves Heuristic Search for Cost-Sensitive Learning. Valentina Bayer Zubek, Thomas G. Dietterich. ICML 2002, 19-26. Web SearchBibTeX
65Action Refinement in Reinforcement Learning by Probability Smoothing. Thomas G. Dietterich, Dídac Busquets, Ramon López de Mántaras, Carles Sierra. ICML 2002, 107-114. Web SearchBibTeX
64Bias-Variance Analysis and Ensembles of SVM. Giorgio Valentini, Thomas G. Dietterich. Multiple Classifier Systems 2002, 222-231. Web SearchBibTeXDownload
63Machine Learning for Sequential Data: A Review. Thomas G. Dietterich. SSPR/SPR 2002, 15-30. Web SearchBibTeXDownload
2001
62Support Vectors for Reinforcement Learning. Thomas G. Dietterich, Xin Wang. ECML 2001, 600. Web SearchBibTeXDownload
61Batch Value Function Approximation via Support Vectors. Thomas G. Dietterich, Xin Wang. NIPS 2001, 1491-1498. Web SearchBibTeXDownload
60Stabilizing Value Function Approximation with the BFBP Algorithm. Xin Wang, Thomas G. Dietterich. NIPS 2001, 1587-1594. Web SearchBibTeXDownload
2000
59The Divide-and-Conquer Manifesto. Thomas G. Dietterich. ALT 2000, 13-26. Web SearchBibTeXDownload
58A Divide and Conquer Approach to Learning from Prior Knowledge. Eric Chown, Thomas G. Dietterich. ICML 2000, 143-150. Web SearchBibTeX
57Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. Dragos D. Margineantu, Thomas G. Dietterich. ICML 2000, 583-590. Web SearchBibTeX
56Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Thomas G. Dietterich. J. Artif. Intell. Res. (JAIR) (13): 227-303 (2000). Web SearchBibTeXDownload
55Mining IC test data to optimize VLSI testing. Tony Fountain, Thomas G. Dietterich, Bill Sudyka. KDD 2000, 18-25. Web SearchBibTeXDownload
54An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Thomas G. Dietterich. Machine Learning (40): 139-157 (2000). Web SearchBibTeXDownload
53Ensemble Methods in Machine Learning. Thomas G. Dietterich. Multiple Classifier Systems 2000, 1-15. Web SearchBibTeXDownload
52A POMDP Approximation Algorithm That Anticipates the Need to Observe. Valentina Bayer Zubek, Thomas G. Dietterich. PRICAI 2000, 521-532. Web SearchBibTeXDownload
51An Overview of MAXQ Hierarchical Reinforcement Learning. Thomas G. Dietterich. SARA 2000, 26-44. Web SearchBibTeXDownload
1999
50Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Thomas G. Dietterich. CoRR (cs.LG/9905014) (1999). Web SearchBibTeXDownload
49State Abstraction in MAXQ Hierarchical Reinforcement Learning. Thomas G. Dietterich. NIPS 1999, 994-1000. Web SearchBibTeXDownload
1998
48The MAXQ Method for Hierarchical Reinforcement Learning. Thomas G. Dietterich. ICML 1998, 118-126. Web SearchBibTeX
47Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms. Thomas G. Dietterich. Neural Computation (10): 1895-1923 (1998). Web SearchBibTeXDownload
1997
46Machine-Learning Research. Thomas G. Dietterich. AI Magazine (18): 97-136 (1997). Web SearchBibTeXDownload
45Solving the Multiple Instance Problem with Axis-Parallel Rectangles. Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez. Artif. Intell. (89): 31-71 (1997). Web SearchBibTeXDownload
44Hierarchical Explanation-Based Reinforcement Learning. Prasad Tadepalli, Thomas G. Dietterich. ICML 1997, 358-366. Web SearchBibTeX
43Pruning Adaptive Boosting. Dragos D. Margineantu, Thomas G. Dietterich. ICML 1997, 211-218. Web SearchBibTeX
42Explanation-Based Learning and Reinforcement Learning: A Unified View. Thomas G. Dietterich, Nicholas S. Flann. Machine Learning (28): 169-210 (1997). Web SearchBibTeXDownload
1996
41Machine Learning. Thomas G. Dietterich, Shing-Hwang Doong, Chih-Hung Wu. ACM Comput. Surv. (28): 3 (1996). Web SearchBibTeXDownload
40Applying the Waek Learning Framework to Understand and Improve C4.5. Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour. ICML 1996, 96-104. Web SearchBibTeX
1995
39Overfitting and Undercomputing in Machine Learning. Thomas G. Dietterich. ACM Comput. Surv. (27): 326-327 (1995). Web SearchBibTeXDownload
38Solving Multiclass Learning Problems via Error-Correcting Output Codes. Thomas G. Dietterich, Ghulum Bakiri. CoRR (cs.AI/9501101): 263-286 (1995). Web SearchBibTeXDownload
37Explanation-Based Learning and Reinforcement Learning: A Unified View. Thomas G. Dietterich, Nicholas S. Flann. ICML 1995, 176-184. Web SearchBibTeX
36Error-Correcting Output Coding Corrects Bias and Variance. Eun Bae Kong, Thomas G. Dietterich. ICML 1995, 313-321. Web SearchBibTeX
35A Reinforcement Learning Approach to job-shop Scheduling. Wei Zhang, Thomas G. Dietterich. IJCAI 1995, 1114-1120. Web SearchBibTeX
34A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping. Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri. Machine Learning (18): 51-80 (1995). Web SearchBibTeXDownload
33An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms. Dietrich Wettschereck, Thomas G. Dietterich. Machine Learning (19): 5-27 (1995). Web SearchBibTeXDownload
32High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network. Wei Zhang, Thomas G. Dietterich. NIPS 1995, 1024-1030. Web SearchBibTeXDownload
1994
31Learning Boolean Concepts in the Presence of Many Irrelevant Features. Hussein Almuallim, Thomas G. Dietterich. Artif. Intell. (69): 279-305 (1994). Web SearchBibTeXDownload
30Compass: A shape-based machine learning tool for drug design. Ajay N. Jain, Thomas G. Dietterich, Richard H. Lathrop, David Chapman, Roger E. Critchlow Jr., Barr E. Bauer, Teresa A. Webster, Tomás Lozano-Pérez. Journal of Computer-Aided Molecular Design (8): 635-652 (1994). Web SearchBibTeXDownload
29Editorial: New Editorial Board Members. Thomas G. Dietterich. Machine Learning (16): 5-6 (1994). Web SearchBibTeX
1993
28Locally Adaptive Nearest Neighbor Algorithms. Dietrich Wettschereck, Thomas G. Dietterich. NIPS 1993, 184-191. Web SearchBibTeXDownload
27Memory-Based Methods for Regression and Classification. Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore. NIPS 1993, 1165-1166. Web SearchBibTeXDownload
26A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction. Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez. NIPS 1993, 216-223. Web SearchBibTeXDownload
1992
25On Learning More Concepts. Hussein Almuallim, Thomas G. Dietterich. ML 1992, 11-19. Web SearchBibTeX
1991
24Learning with Many Irrelevant Features. Hussein Almuallim, Thomas G. Dietterich. AAAI 1991, 547-552. Web SearchBibTeX
23Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs. Thomas G. Dietterich, Ghulum Bakiri. AAAI 1991, 572-577. Web SearchBibTeX
22Knowledge Compilation to Speed Up Numerical Optimisation. Giuseppe Cerbone, Thomas G. Dietterich. AI*IA 1991, 208-217. Web SearchBibTeXDownload
21Knowledge Compilation: A Symposium. Ashok K. Goel, Tom Bylander, B. Chandrasekaran, Thomas G. Dietterich, Richard M. Keller, Chris Tong. IEEE Expert (6): 71-93 (1991). Web SearchBibTeXDownload
20Knowledge Compilation to Speed Up Numerical Optimization. Giuseppe Cerbone, Thomas G. Dietterich. ML 1991, 600-604. Web SearchBibTeX
19Machine Learning in Engineering Automation. Steve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu. ML 1991, 577-580. Web SearchBibTeX
18Improving the Performance of Radial Basis Function Networks by Learning Center Locations. Dietrich Wettschereck, Thomas G. Dietterich. NIPS 1991, 1133-1140. Web SearchBibTeXDownload
1990
17Exploratory Research in Machine Learning. Thomas G. Dietterich. Machine Learning (5): 5-9 (1990). Web SearchBibTeXDownload
16A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping. Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri. ML 1990, 24-31. Web SearchBibTeX
1989
15A Study of Explanation-Based Methods for Inductive Learning. Nicholas S. Flann, Thomas G. Dietterich. Machine Learning (4): 187-226 (1989). Web SearchBibTeXDownload
14News and Notes. Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins. Machine Learning (3): 373-375 (1989). Web SearchBibTeX
13Limitations on Inductive Learning. Thomas G. Dietterich. ML 1989, 124-128. Web SearchBibTeX
12What Good Are Experiments?. Ritchey A. Ruff, Thomas G. Dietterich. ML 1989, 109-112. Web SearchBibTeX
1988
11An Efficient ATMS for Equivalence Relations. Caroline N. Koff, Nicholas S. Flann, Thomas G. Dietterich. AAAI 1988, 182-187. Web SearchBibTeX
10News and Notes. Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins. Machine Learning (3): 247-249 (1988). Web SearchBibTeX
1987
9Forward Chaining Logic Programming with the ATMS. Nicholas S. Flann, Thomas G. Dietterich, Dan R. Corpon. AAAI 1987, 24-29. Web SearchBibTeX
8News and Notes. Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins. Machine Learning (2): 75-96 (1987). Web SearchBibTeX
1986
7Selecting Appropriate Representations for Learning from Examples. Nicholas S. Flann, Thomas G. Dietterich. AAAI 1986, 460-466. Web SearchBibTeX
6Learning at the Knowledge Level. Thomas G. Dietterich. Machine Learning (1): 287-316 (1986). Web SearchBibTeXDownload
5News and Notes. Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins. Machine Learning (1): 355-358 (1986). Web SearchBibTeX
1985
4Discovering Patterns in Sequences of Events. Thomas G. Dietterich, Ryszard S. Michalski. Artif. Intell. (25): 187-232 (1985). Web SearchBibTeXDownload
1984
3Learning About Systems That Contain State Variables. Thomas G. Dietterich. AAAI 1984, 96-100. Web SearchBibTeX
1981
2Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. Thomas G. Dietterich, Ryszard S. Michalski. Artif. Intell. (16): 257-294 (1981). Web SearchBibTeXDownload
1980
1Applying General Induction Methods to the Card Game Eleusis. Thomas G. Dietterich. AAAI 1980, 218-220. Web SearchBibTeXDownload
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References
1. ^ ICML-2003 Committees - Retrieved 2009-11-21 - details
2. ^ ICML-2003 Committees - Retrieved 2009-11-21 - details
3. ^ http://www.cse.unsw.edu.au/~icml2002/refs.html - Retrieved 2011-03-19 - details
4. ^ NC State Computer Science: 2011-2012 Scheduled Seminars - Retrieved 2012-01-12 - details
5. ^ UT-Austin Data Mining Seminar Schedule Abstracts - Retrieved 2010-09-28 - details
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