Thomas G. Dietterich

Oregon State University, School of Electrical Engineering and Computer Science

	2012
j46		Xiaoqin Zhang, Bhavesh Shrestha, Sung Wook Yoon, Subbarao Kambhampati, Phillip DiBona, Jinhong K. Guo, Daniel McFarlane, Martin O. Hofmann, Kenneth R. Whitebread, Darren Scott Appling, Elizabeth T. Whitaker, Ethan Trewhitt, Li Ding, James Michaelis, Deborah L. McGuinness, James A. Hendler, Janardhan Rao Doppa, Charles Parker, Thomas G. Dietterich, Prasad Tadepalli, Weng-Keen Wong, Derek T. Green, Antons Rebguns, Diana F. Spears, Ugur Kuter, Geoffrey Levine, Gerald DeJong, Reid MacTavish, Santiago Ontañón, Jainarayan Radhakrishnan, Ashwin Ram, Hala Mostafa, Huzaifa Zafar, Chongjie Zhang, Daniel D. Corkill, Victor R. Lesser, Zhexuan Song: An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration. ACM TIST 3(4): 75 (2012)
c90		Thomas G. Dietterich, Ethan W. Dereszynski, Rebecca A. Hutchinson, Daniel R. Sheldon: Machine learning for computational sustainability. IGCC 2012: 1
c89		Li-Ping Liu, Thomas G. Dietterich: A Conditional Multinomial Mixture Model for Superset Label Learning. NIPS 2012: 557-565
c88		Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich, Alan Fern: Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games. UAI 2012: 367-376
c87		Kshitij Judah, Alan Fern, Thomas G. Dietterich: Active Imitation Learning via Reduction to I.I.D. Active Learning. UAI 2012: 428-437
i8		Ethan W. Dereszynski, Thomas G. Dietterich: Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. CoRR abs/1206.5250 (2012)
i7		Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich: Learning from Sparse Data by Exploiting Monotonicity Constraints. CoRR abs/1207.1364 (2012)
i6		Kshitij Judah, Alan Fern, Thomas G. Dietterich: Active Imitation Learning via Reduction to I.I.D. Active Learning. CoRR abs/1210.4876 (2012)
i5		Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich, Alan Fern: Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games. CoRR abs/1210.4880 (2012)
	2011
j45		Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich: Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning. AI Magazine 32(1): 35-50 (2011)
j44		Janardhan Rao Doppa, Shahed Sorower, Mohammad NasrEsfahani, Walker Orr, Thomas G. Dietterich, Xiaoli Fern, Prasad Tadepalli, Jed Irvine: Learning Rules from Incomplete Examples via Implicit Mention Models. Journal of Machine Learning Research - Proceedings Track 20: 197-212 (2011)
j43		Xinlong Bao, Thomas G. Dietterich: FolderPredictor: Reducing the cost of reaching the right folder. ACM TIST 2(1): 8 (2011)
j42		Ethan W. Dereszynski, Thomas G. Dietterich: Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns. TOSN 8(1): 3 (2011)
c86		Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich: Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI 2011
c85		Ethan W. Dereszynski, Jesse Hostetler, Alan Fern, Thomas G. Dietterich, Thao-Trang Hoang, Mark Udarbe: Learning Probabilistic Behavior Models in Real-Time Strategy Games. AIIDE 2011
c84		Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa, Walker Orr, Prasad Tadepalli, Xiaoli Fern: Inverting Grice's Maxims to Learn Rules from Natural Language Extractions. NIPS 2011: 1053-1061
c83		Daniel R. Sheldon, Thomas G. Dietterich: Collective Graphical Models. NIPS 2011: 1161-1169
i4		Valentina Bayer Zubek, Thomas G. Dietterich: Integrating Learning from Examples into the Search for Diagnostic Policies. CoRR abs/1109.2127 (2011)
	2010
c82		Kshitij Judah, Saikat Roy, Alan Fern, Thomas G. Dietterich: Reinforcement Learning Via Practice and Critique Advice. AAAI 2010
c81		Carlos Jensen, Heather Lonsdale, Eleanor Wynn, Jill Cao, Michael Slater, Thomas G. Dietterich: The life and times of files and information: a study of desktop provenance. CHI 2010: 767-776
c80		Natalia Larios, Bilge Soran, Linda G. Shapiro, Gonzalo Martínez-Muñoz, Junyuan Lin, Thomas G. Dietterich: Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification. ICPR 2010: 2624-2627
p2		Paul Barford, Marc Dacier, Thomas G. Dietterich, Matt Fredrikson, Jonathon T. Giffin, Sushil Jajodia, Somesh Jha, Jason H. Li, Peng Liu, Peng Ning, Xinming Ou, Dawn Song, Laura Strater, Vipin Swarup, George P. Tadda, C. Wang, John Yen: Cyber SA: Situational Awareness for Cyber Defense. Cyber Situational Awareness 2010: 3-13
p1		Thomas G. Dietterich, Xinlong Bao, Victoria Keiser, Jianqiang Shen: Machine Learning Methods for High Level Cyber Situation Awareness. Cyber Situational Awareness 2010: 227-247
	2009
j41		Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret M. Burnett, Thomas G. Dietterich, Erin Sullivan, Jonathan L. Herlocker: Interacting meaningfully with machine learning systems: Three experiments. Int. J. Hum.-Comput. Stud. 67(8): 639-662 (2009)
j40		Jianqiang Shen, Thomas G. Dietterich: A family of large margin linear classifiers and its application in dynamic environments. Statistical Analysis and Data Mining 2(5-6): 328-345 (2009)
c79		Thomas G. Dietterich: Machine Learning and Ecosystem Informatics: Challenges and Opportunities. ACML 2009: 1-5
c78		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: Dictionary-free categorization of very similar objects via stacked evidence trees. CVPR 2009: 549-556
c77		Xiaoqin Zhang, Sung Wook Yoon, Phillip DiBona, Darren Scott Appling, Li Ding, Janardhan Rao Doppa, Derek T. Green, Jinhong K. Guo, Ugur Kuter, Geoffrey Levine, Reid MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontañón, Charles Parker, Jainarayan Radhakrishnan, Antons 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 T. Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, Kenneth R. Whitebread: An Ensemble Learning and Problem Solving Architecture for Airspace Management. IAAI 2009
c76		Wei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich: Learning non-redundant codebooks for classifying complex objects. ICML 2009: 156
c75		Thomas G. Dietterich: Machine Learning in Ecosystem Informatics and Sustainability. IJCAI 2009: 8-13
c74		Jianqiang Shen, Jed Irvine, Xinlong Bao, Michael Goodman, Stephen Kolibaba, Anh Tran, Fredric Carl, Brenton Kirschner, Simone Stumpf, Thomas G. Dietterich: Detecting and correcting user activity switches: algorithms and interfaces. IUI 2009: 117-126
c73		Jianqiang Shen, Erin Fitzhenry, Thomas G. Dietterich: Discovering frequent work procedures from resource connections. IUI 2009: 277-286
c72		Jianqiang Shen, Thomas G. Dietterich: A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. SDM 2009: 164-172
	2008
j39		Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern: Learning first-order probabilistic models with combining rules. Ann. Math. Artif. Intell. 54(1-3): 223-256 (2008)
j38		Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli: Structured machine learning: the next ten years. Machine Learning 73(1): 3-23 (2008)
j37		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: Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects. Mach. Vis. Appl. 19(2): 105-123 (2008)
c71		Thomas G. Dietterich, Xinlong Bao: Integrating Multiple Learning Components through Markov Logic. AAAI 2008: 622-627
c70		Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich: Automatic discovery and transfer of MAXQ hierarchies. ICML 2008: 648-655
c69		Wei Zhang, Thomas G. Dietterich: Learning visual dictionaries and decision lists for object recognition. ICPR 2008: 1-4
c68		Michael Wynkoop, Thomas G. Dietterich: Learning MDP Action Models Via Discrete Mixture Trees. ECML/PKDD (2) 2008: 597-612
e5		Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen Muggleton (Eds.): Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007. Dagstuhl Seminar Proceedings 07161, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008
	2007
j36		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: AAAI-07 Workshop Reports. AI Magazine 28(4): 119-128 (2007)
c67		Thomas G. Dietterich: Machine Learning in Ecosystem Informatics. ALT 2007: 10-11
c66		Hongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro: Principal Curvature-Based Region Detector for Object Recognition. CVPR 2007
c65		Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen Muggleton: 07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007
c64		Thomas G. Dietterich: Machine Learning in Ecosystem Informatics. Discovery Science 2007: 9-25
c63		Simone Stumpf, Margaret M. Burnett, Thomas G. Dietterich: Improving Intelligent Assistants for Desktop Activities. Interaction Challenges for Intelligent Assistants 2007: 119-121
c62		Jianqiang Shen, Lida Li, Thomas G. Dietterich: Real-Time Detection of Task Switches of Desktop Users. IJCAI 2007: 2868-2873
c61		Simone Stumpf, Vidya Rajaram, Lida Li, Margaret M. Burnett, Thomas G. Dietterich, Erin Sullivan, Russell Drummond, Jonathan L. Herlocker: Toward harnessing user feedback for machine learning. IUI 2007: 82-91
c60		Jianqiang Shen, Thomas G. Dietterich: Active EM to reduce noise in activity recognition. IUI 2007: 132-140
c59		Ethan W. Dereszynski, Thomas G. Dietterich: Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. UAI 2007: 75-82
c58		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: Automated Insect Identification through Concatenated Histograms of Local Appearance Features. WACV 2007: 26
	2006
c57		Wei Zhang, Hongli Deng, Thomas G. Dietterich, Eric N. Mortensen: A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions. ICPR (1) 2006: 778-782
c56		Jianqiang Shen, Lida Li, Thomas G. Dietterich, Jonathan L. Herlocker: A hybrid learning system for recognizing user tasks from desktop activities and email messages. IUI 2006: 86-92
c55		Xinlong Bao, Jonathan L. Herlocker, Thomas G. Dietterich: Fewer clicks and less frustration: reducing the cost of reaching the right folder. IUI 2006: 178-185
e4		Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton (Eds.): Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005. Dagstuhl Seminar Proceedings 05051, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2006
	2005
j35		Valentina Bayer Zubek, Thomas G. Dietterich: Integrating Learning from Examples into the Search for Diagnostic Policies. J. Artif. Intell. Res. (JAIR) 24: 263-303 (2005)
c54		Simone Stumpf, Xinlong Bao, Anton N. Dragunov, Thomas G. Dietterich, Jonathan L. Herlocker, Kevin Johnsrude, Lida Li, Jianqiang Shen: The TaskTracker System. AAAI 2005: 1712-1713
c53		Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton: 05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005
c52		Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton: 05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005
c51		Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar: Learning first-order probabilistic models with combining rules. ICML 2005: 609-616
c50		Anton N. Dragunov, Thomas G. Dietterich, Kevin Johnsrude, Matthew R. McLaughlin, Lida Li, Jonathan L. Herlocker: TaskTracer: a desktop environment to support multi-tasking knowledge workers. IUI 2005: 75-82
c49		Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich: Learning from Sparse Data by Exploiting Monotonicity Constraints. UAI 2005: 18-26
	2004
j34		Giorgio Valentini, Thomas G. Dietterich: Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods. Journal of Machine Learning Research 5: 725-775 (2004)
c48		Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov: Training conditional random fields via gradient tree boosting. ICML 2004
c47		Pengcheng Wu, Thomas G. Dietterich: Improving SVM accuracy by training on auxiliary data sources. ICML 2004
	2003
c46		Giorgio Valentini, Thomas G. Dietterich: Low Bias Bagged Support Vector Machines. ICML 2003: 752-759
c45		Xin Wang, Thomas G. Dietterich: Model-based Policy Gradient Reinforcement Learning. ICML 2003: 776-783
	2002
c44		Dídac Busquets, Ramon López de Mántaras, Carles Sierra, Thomas G. Dietterich: A Multi-agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation. CCIA 2002: 269-281
c43		Valentina Bayer Zubek, Thomas G. Dietterich: Pruning Improves Heuristic Search for Cost-Sensitive Learning. ICML 2002: 19-26
c42		Thomas G. Dietterich, Dídac Busquets, Ramon López de Mántaras, Carles Sierra: Action Refinement in Reinforcement Learning by Probability Smoothing. ICML 2002: 107-114
c41		Giorgio Valentini, Thomas G. Dietterich: Bias-Variance Analysis and Ensembles of SVM. Multiple Classifier Systems 2002: 222-231
c40		Thomas G. Dietterich: Machine Learning for Sequential Data: A Review. SSPR/SPR 2002: 15-30
	2001
c39		Thomas G. Dietterich, Xin Wang: Support Vectors for Reinforcement Learning. ECML 2001: 600
c38		Thomas G. Dietterich, Xin Wang: Batch Value Function Approximation via Support Vectors. NIPS 2001: 1491-1498
c37		Xin Wang, Thomas G. Dietterich: Stabilizing Value Function Approximation with the BFBP Algorithm. NIPS 2001: 1587-1594
c36		Thomas G. Dietterich, Xin Wang: Support Vectors for Reinforcement Learning. PKDD 2001: 492
e3		Todd K. Leen, Thomas G. Dietterich, Volker Tresp (Eds.): Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA. MIT Press 2001
e2		Thomas G. Dietterich, Suzanna Becker, Zoubin Ghahramani (Eds.): Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]. MIT Press 2001
	2000
j33		Thomas G. Dietterich: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. J. Artif. Intell. Res. (JAIR) 13: 227-303 (2000)
j32		Thomas G. Dietterich: An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40(2): 139-157 (2000)
c35		Thomas G. Dietterich: The Divide-and-Conquer Manifesto. ALT 2000: 13-26
c34		Eric Chown, Thomas G. Dietterich: A Divide and Conquer Approach to Learning from Prior Knowledge. ICML 2000: 143-150
c33		Dragos D. Margineantu, Thomas G. Dietterich: Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. ICML 2000: 583-590
c32		Tony Fountain, Thomas G. Dietterich, Bill Sudyka: Mining IC test data to optimize VLSI testing. KDD 2000: 18-25
c31		Thomas G. Dietterich: Ensemble Methods in Machine Learning. Multiple Classifier Systems 2000: 1-15
c30		Valentina Bayer Zubek, Thomas G. Dietterich: A POMDP Approximation Algorithm That Anticipates the Need to Observe. PRICAI 2000: 521-532
c29		Thomas G. Dietterich: An Overview of MAXQ Hierarchical Reinforcement Learning. SARA 2000: 26-44
	1999
c28		Thomas G. Dietterich: State Abstraction in MAXQ Hierarchical Reinforcement Learning. NIPS 1999: 994-1000
i3		Thomas G. Dietterich: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. CoRR cs.LG/9905014 (1999)
i2		Thomas G. Dietterich: State Abstraction in MAXQ Hierarchical Reinforcement Learning. CoRR cs.LG/9905015 (1999)
	1998
j31		Thomas G. Dietterich: Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7): 1895-1923 (1998)
c27		Thomas G. Dietterich: The MAXQ Method for Hierarchical Reinforcement Learning. ICML 1998: 118-126
	1997
j30		Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez: Solving the Multiple Instance Problem with Axis-Parallel Rectangles. Artif. Intell. 89(1-2): 31-71 (1997)
j29		Thomas G. Dietterich: Machine-Learning Research. AI Magazine 18(4): 97-136 (1997)
j28		Thomas G. Dietterich, Nicholas S. Flann: Explanation-Based Learning and Reinforcement Learning: A Unified View. Machine Learning 28(2-3): 169-210 (1997)
c26		Dragos D. Margineantu, Thomas G. Dietterich: Pruning Adaptive Boosting. ICML 1997: 211-218
c25		Prasad Tadepalli, Thomas G. Dietterich: Hierarchical Explanation-Based Reinforcement Learning. ICML 1997: 358-366
	1996
j27		Thomas G. Dietterich: Machine Learning. ACM Comput. Surv. 28(4es): 3 (1996)
j26		Thomas G. Dietterich: Editorial. Machine Learning 22(1-3): 5-6 (1996)
c24		Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour: Applying the Waek Learning Framework to Understand and Improve C4.5. ICML 1996: 96-104
	1995
j25		Thomas G. Dietterich: Overfitting and Undercomputing in Machine Learning. ACM Comput. Surv. 27(3): 326-327 (1995)
j24		Thomas G. Dietterich, Ghulum Bakiri: Solving Multiclass Learning Problems via Error-Correcting Output Codes. J. Artif. Intell. Res. (JAIR) 2: 263-286 (1995)
j23		Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri: A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping. Machine Learning 18(1): 51-80 (1995)
j22		Dietrich Wettschereck, Thomas G. Dietterich: An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms. Machine Learning 19(1): 5-27 (1995)
c23		Thomas G. Dietterich, Nicholas S. Flann: Explanation-Based Learning and Reinforcement Learning: A Unified View. ICML 1995: 176-184
c22		Eun Bae Kong, Thomas G. Dietterich: Error-Correcting Output Coding Corrects Bias and Variance. ICML 1995: 313-321
c21		Wei Zhang, Thomas G. Dietterich: A Reinforcement Learning Approach to job-shop Scheduling. IJCAI 1995: 1114-1120
c20		Wei Zhang, Thomas G. Dietterich: High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network. NIPS 1995: 1024-1030
i1		Thomas G. Dietterich, Ghulum Bakiri: Solving Multiclass Learning Problems via Error-Correcting Output Codes. CoRR cs.AI/9501101 (1995)
	1994
j21		Hussein Almuallim, Thomas G. Dietterich: Learning Boolean Concepts in the Presence of Many Irrelevant Features. Artif. Intell. 69(1-2): 279-305 (1994)
j20		Ajay N. Jain, Thomas G. Dietterich, Richard H. Lathrop, David Chapman, Roger E. Critchlow Jr., Barr E. Bauer, Teresa A. Webster, Tomás Lozano-Pérez: Compass: A shape-based machine learning tool for drug design. Journal of Computer-Aided Molecular Design 8(6): 635-652 (1994)
j19		Thomas G. Dietterich: Editorial: New Editorial Board Members. Machine Learning 16(1-2): 5-6 (1994)
	1993
j18		Thomas G. Dietterich: Editorial. Machine Learning 10: 5 (1993)
c19		Dietrich Wettschereck, Thomas G. Dietterich: Locally Adaptive Nearest Neighbor Algorithms. NIPS 1993: 184-191
c18		Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez: A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction. NIPS 1993: 216-223
c17		Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore: Memory-Based Methods for Regression and Classification. NIPS 1993: 1165-1166
	1992
j17		Thomas G. Dietterich: Editorial. Machine Learning 8: 105 (1992)
c16		Hussein Almuallim, Thomas G. Dietterich: On Learning More Concepts. ML 1992: 11-19
	1991
j16		Ashok K. Goel, Tom Bylander, B. Chandrasekaran, Thomas G. Dietterich, Richard M. Keller, Chris Tong: Knowledge Compilation: A Symposium. IEEE Expert 6(2): 71-93 (1991)
c15		Hussein Almuallim, Thomas G. Dietterich: Learning with Many Irrelevant Features. AAAI 1991: 547-552
c14		Thomas G. Dietterich, Ghulum Bakiri: Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs. AAAI 1991: 572-577
c13		Giuseppe Cerbone, Thomas G. Dietterich: Knowledge Compilation to Speed Up Numerical Optimisation. AI*IA 1991: 208-217
c12		Steve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu: Machine Learning in Engineering Automation. ML 1991: 577-580
c11		Giuseppe Cerbone, Thomas G. Dietterich: Knowledge Compilation to Speed Up Numerical Optimization. ML 1991: 600-604
c10		Dietrich Wettschereck, Thomas G. Dietterich: Improving the Performance of Radial Basis Function Networks by Learning Center Locations. NIPS 1991: 1133-1140
	1990
j15		Thomas G. Dietterich: Exploratory Research in Machine Learning. Machine Learning 5: 5-9 (1990)
c9		Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri: A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping. ML 1990: 24-31
e1		Howard E. Shrobe, Thomas G. Dietterich, William R. Swartout (Eds.): Proceedings of the 8th National Conference on Artificial Intelligence. Boston, Massachusetts, July 29 - August 3, 1990, 2 Volumes. AAAI Press / The MIT Press 1990, isbn 0-262-51057-X
	1989
j14		Thomas G. Dietterich: News and Notes. Machine Learning 3: 373-375 (1989)
j13		Thomas G. Dietterich: News and Notes. Machine Learning 4: 107-109 (1989)
j12		Nicholas S. Flann, Thomas G. Dietterich: A Study of Explanation-Based Methods for Inductive Learning. Machine Learning 4: 187-226 (1989)
c8		Ritchey A. Ruff, Thomas G. Dietterich: What Good Are Experiments?. ML 1989: 109-112
c7		Thomas G. Dietterich: Limitations on Inductive Learning. ML 1989: 124-128
	1988
j11		Thomas G. Dietterich: News and Notes. Machine Learning 3: 247-249 (1988)
c6		Caroline N. Koff, Nicholas S. Flann, Thomas G. Dietterich: An Efficient ATMS for Equivalence Relations. AAAI 1988: 182-187
	1987
j10		Thomas G. Dietterich: News and Notes. Machine Learning 2(1): 75-96 (1987)
j9		Thomas G. Dietterich: News and Notes. Machine Learning 2(2): 191-192 (1987)
j8		Thomas G. Dietterich: News and Notes. Machine Learning 2(3): 277-278 (1987)
j7		Thomas G. Dietterich: News and Notes. Machine Learning 2(4): 397-398 (1987)
c5		Nicholas S. Flann, Thomas G. Dietterich, Dan R. Corpon: Forward Chaining Logic Programming with the ATMS. AAAI 1987: 24-29
	1986
j6		Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins: News and Notes. Machine Learning 1(2): 227-242 (1986)
j5		Thomas G. Dietterich: Learning at the Knowledge Level. Machine Learning 1(3): 287-316 (1986)
j4		Yves Kodratoff, Gheorghe Tecuci, Thomas G. Dietterich: News and Notes. Machine Learning 1(3): 355-358 (1986)
j3		Thomas G. Dietterich: News and Notes. Machine Learning 1(4): 453-454 (1986)
c4		Nicholas S. Flann, Thomas G. Dietterich: Selecting Appropriate Representations for Learning from Examples. AAAI 1986: 460-466
	1985
j2		Thomas G. Dietterich, Ryszard S. Michalski: Discovering Patterns in Sequences of Events. Artif. Intell. 25(2): 187-232 (1985)
	1984
c3		Thomas G. Dietterich: Learning About Systems That Contain State Variables. AAAI 1984: 96-100
	1981
j1		Thomas G. Dietterich, Ryszard S. Michalski: Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. Artif. Intell. 16(3): 257-294 (1981)
	1980
c2		Thomas G. Dietterich: Applying General Induction Methods to the Card Game Eleusis. AAAI 1980: 218-220
	1979
c1		Thomas G. Dietterich, Ryszard S. Michalski: Learning and Generalization of Characteristic Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. IJCAI 1979: 223-231

Coauthor Index

Colors in the list of coauthors

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