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| 2011 | ||
|---|---|---|
| 141 | Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich: Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI 2011 | |
| 140 | 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 | |
| 139 | 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 | |
| 138 | Daniel R. Sheldon, Thomas G. Dietterich: Collective Graphical Models. NIPS 2011: 1161-1169 | |
| 137 | Xinlong Bao, Thomas G. Dietterich: FolderPredictor: Reducing the cost of reaching the right folder. ACM TIST 2(1): 8 (2011) | |
| 136 | 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) | |
| 135 | Valentina Bayer Zubek, Thomas G. Dietterich: Integrating Learning from Examples into the Search for Diagnostic Policies CoRR abs/1109.2127: (2011) | |
| 134 | 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) | |
| 133 | Ethan W. Dereszynski, Thomas G. Dietterich: Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns. TOSN 8(1): 3 (2011) | |
| 2010 | ||
| 132 | Kshitij Judah, Saikat Roy, Alan Fern, Thomas G. Dietterich: Reinforcement Learning Via Practice and Critique Advice. AAAI 2010 | |
| 131 | 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 | |
| 130 | Natalia Larios, B. Soran, Linda G. Shapiro, Gonzalo Martínez-Muñoz, J. Lin, Thomas G. Dietterich: Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification. ICPR 2010: 2624-2627 | |
| 129 | Thomas G. Dietterich, Xinlong Bao, Victoria Keiser, Jianqiang Shen: Machine Learning Methods for High Level Cyber Situation Awareness. Cyber Situational Awareness 2010: 227-247 | |
| 128 | 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, D. 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 | |
| 2009 | ||
| 127 | Thomas G. Dietterich: Machine Learning and Ecosystem Informatics: Challenges and Opportunities. ACML 2009: 1-5 | |
| 126 | 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 | |
| 125 | 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: An Ensemble Learning and Problem Solving Architecture for Airspace Management. IAAI 2009 | |
| 124 | Wei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich: Learning non-redundant codebooks for classifying complex objects. ICML 2009: 156 | |
| 123 | Thomas G. Dietterich: Machine Learning in Ecosystem Informatics and Sustainability. IJCAI 2009: 8-13 | |
| 122 | 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 | |
| 121 | Jianqiang Shen, Erin Fitzhenry, Thomas G. Dietterich: Discovering frequent work procedures from resource connections. IUI 2009: 277-286 | |
| 120 | Jianqiang Shen, Thomas G. Dietterich: A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. SDM 2009: 164-172 | |
| 119 | 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) | |
| 118 | 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) | |
| 2008 | ||
| 117 | Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen Muggleton: Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007 Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 | |
| 116 | Thomas G. Dietterich, Xinlong Bao: Integrating Multiple Learning Components through Markov Logic. AAAI 2008: 622-627 | |
| 115 | Michael Wynkoop, Thomas G. Dietterich: Learning MDP Action Models Via Discrete Mixture Trees. ECML/PKDD (2) 2008: 597-612 | |
| 114 | Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich: Automatic discovery and transfer of MAXQ hierarchies. ICML 2008: 648-655 | |
| 113 | Wei Zhang, Thomas G. Dietterich: Learning visual dictionaries and decision lists for object recognition. ICPR 2008: 1-4 | |
| 112 | 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) | |
| 111 | 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) | |
| 110 | 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) | |
| 2007 | ||
| 109 | Thomas G. Dietterich: Machine Learning in Ecosystem Informatics. ALT 2007: 10-11 | |
| 108 | Hongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro: Principal Curvature-Based Region Detector for Object Recognition. CVPR 2007 | |
| 107 | Thomas G. Dietterich: Machine Learning in Ecosystem Informatics. Discovery Science 2007: 9-25 | |
| 106 | Jianqiang Shen, Lida Li, Thomas G. Dietterich: Real-Time Detection of Task Switches of Desktop Users. IJCAI 2007: 2868-2873 | |
| 105 | Jianqiang Shen, Thomas G. Dietterich: Active EM to reduce noise in activity recognition. IUI 2007: 132-140 | |
| 104 | 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 | |
| 103 | Simone Stumpf, Margaret M. Burnett, Thomas G. Dietterich: Improving Intelligent Assistants for Desktop Activities. Interaction Challenges for Intelligent Assistants 2007: 119-121 | |
| 102 | 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 | |
| 101 | Ethan W. Dereszynski, Thomas G. Dietterich: Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. UAI 2007: 75-82 | |
| 100 | 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 | |
| 99 | 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) | |
| 2006 | ||
| 98 | Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton: Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005 Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2006 | |
| 97 | 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 | |
| 96 | 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 | |
| 95 | 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 | |
| 2005 | ||
| 94 | 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 | |
| 93 | 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 | |
| 92 | 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 | |
| 91 | 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 | |
| 90 | 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 | |
| 89 | Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich: Learning from Sparse Data by Exploiting Monotonicity Constraints. UAI 2005: 18-26 | |
| 88 | 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) | |
| 2004 | ||
| 87 | Pengcheng Wu, Thomas G. Dietterich: Improving SVM accuracy by training on auxiliary data sources. ICML 2004 | |
| 86 | Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov: Training conditional random fields via gradient tree boosting. ICML 2004 | |
| 85 | 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) | |
| 2003 | ||
| 84 | Giorgio Valentini, Thomas G. Dietterich: Low Bias Bagged Support Vector Machines. ICML 2003: 752-759 | |
| 83 | Xin Wang, Thomas G. Dietterich: Model-based Policy Gradient Reinforcement Learning. ICML 2003: 776-783 | |
| 2002 | ||
| 82 | 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 | |
| 81 | Thomas G. Dietterich, Dídac Busquets, Ramon López de Mántaras, Carles Sierra: Action Refinement in Reinforcement Learning by Probability Smoothing. ICML 2002: 107-114 | |
| 80 | Valentina Bayer Zubek, Thomas G. Dietterich: Pruning Improves Heuristic Search for Cost-Sensitive Learning. ICML 2002: 19-26 | |
| 79 | Giorgio Valentini, Thomas G. Dietterich: Bias-Variance Analysis and Ensembles of SVM. Multiple Classifier Systems 2002: 222-231 | |
| 78 | Thomas G. Dietterich: Machine Learning for Sequential Data: A Review. SSPR/SPR 2002: 15-30 | |
| 2001 | ||
| 77 | Todd K. Leen, Thomas G. Dietterich, Volker Tresp: Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA MIT Press 2001 | |
| 76 | Thomas G. Dietterich, Suzanna Becker, Zoubin Ghahramani: 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 | |
| 75 | Thomas G. Dietterich, Xin Wang: Support Vectors for Reinforcement Learning. ECML 2001: 600 | |
| 74 | Thomas G. Dietterich, Xin Wang: Batch Value Function Approximation via Support Vectors. NIPS 2001: 1491-1498 | |
| 73 | Xin Wang, Thomas G. Dietterich: Stabilizing Value Function Approximation with the BFBP Algorithm. NIPS 2001: 1587-1594 | |
| 72 | Thomas G. Dietterich, Xin Wang: Support Vectors for Reinforcement Learning. PKDD 2001: 492 | |
| 2000 | ||
| 71 | Thomas G. Dietterich: The Divide-and-Conquer Manifesto. ALT 2000: 13-26 | |
| 70 | Eric Chown, Thomas G. Dietterich: A Divide and Conquer Approach to Learning from Prior Knowledge. ICML 2000: 143-150 | |
| 69 | Dragos D. Margineantu, Thomas G. Dietterich: Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. ICML 2000: 583-590 | |
| 68 | Tony Fountain, Thomas G. Dietterich, Bill Sudyka: Mining IC test data to optimize VLSI testing. KDD 2000: 18-25 | |
| 67 | Thomas G. Dietterich: Ensemble Methods in Machine Learning. Multiple Classifier Systems 2000: 1-15 | |
| 66 | Valentina Bayer Zubek, Thomas G. Dietterich: A POMDP Approximation Algorithm That Anticipates the Need to Observe. PRICAI 2000: 521-532 | |
| 65 | Thomas G. Dietterich: An Overview of MAXQ Hierarchical Reinforcement Learning. SARA 2000: 26-44 | |
| 64 | Thomas G. Dietterich: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. J. Artif. Intell. Res. (JAIR) 13: 227-303 (2000) | |
| 63 | 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) | |
| 1999 | ||
| 62 | Thomas G. Dietterich: State Abstraction in MAXQ Hierarchical Reinforcement Learning. NIPS 1999: 994-1000 | |
| 61 | Thomas G. Dietterich: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition CoRR cs.LG/9905014: (1999) | |
| 60 | Thomas G. Dietterich: State Abstraction in MAXQ Hierarchical Reinforcement Learning CoRR cs.LG/9905015: (1999) | |
| 1998 | ||
| 59 | Thomas G. Dietterich: The MAXQ Method for Hierarchical Reinforcement Learning. ICML 1998: 118-126 | |
| 58 | Thomas G. Dietterich: Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7): 1895-1923 (1998) | |
| 1997 | ||
| 57 | Dragos D. Margineantu, Thomas G. Dietterich: Pruning Adaptive Boosting. ICML 1997: 211-218 | |
| 56 | Prasad Tadepalli, Thomas G. Dietterich: Hierarchical Explanation-Based Reinforcement Learning. ICML 1997: 358-366 | |
| 55 | Thomas G. Dietterich: Machine-Learning Research. AI Magazine 18(4): 97-136 (1997) | |
| 54 | 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) | |
| 53 | Thomas G. Dietterich, Nicholas S. Flann: Explanation-Based Learning and Reinforcement Learning: A Unified View. Machine Learning 28(2-3): 169-210 (1997) | |
| 1996 | ||
| 52 | Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour: Applying the Waek Learning Framework to Understand and Improve C4.5. ICML 1996: 96-104 | |
| 51 | Thomas G. Dietterich: Machine Learning. ACM Comput. Surv. 28(4es): 3 (1996) | |
| 50 | Thomas G. Dietterich: Editorial. Machine Learning 22(1-3): 5-6 (1996) | |
| 1995 | ||
| 49 | Thomas G. Dietterich, Nicholas S. Flann: Explanation-Based Learning and Reinforcement Learning: A Unified View. ICML 1995: 176-184 | |
| 48 | Eun Bae Kong, Thomas G. Dietterich: Error-Correcting Output Coding Corrects Bias and Variance. ICML 1995: 313-321 | |
| 47 | Wei Zhang, Thomas G. Dietterich: A Reinforcement Learning Approach to job-shop Scheduling. IJCAI 1995: 1114-1120 | |
| 46 | Wei Zhang, Thomas G. Dietterich: High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network. NIPS 1995: 1024-1030 | |
| 45 | Thomas G. Dietterich: Overfitting and Undercomputing in Machine Learning. ACM Comput. Surv. 27(3): 326-327 (1995) | |
| 44 | Thomas G. Dietterich, Ghulum Bakiri: Solving Multiclass Learning Problems via Error-Correcting Output Codes CoRR cs.AI/9501101: (1995) | |
| 43 | Thomas G. Dietterich, Ghulum Bakiri: Solving Multiclass Learning Problems via Error-Correcting Output Codes. J. Artif. Intell. Res. (JAIR) 2: 263-286 (1995) | |
| 42 | 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) | |
| 41 | Dietrich Wettschereck, Thomas G. Dietterich: An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms. Machine Learning 19(1): 5-27 (1995) | |
| 1994 | ||
| 40 | Hussein Almuallim, Thomas G. Dietterich: Learning Boolean Concepts in the Presence of Many Irrelevant Features. Artif. Intell. 69(1-2): 279-305 (1994) | |
| 39 | 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) | |
| 38 | Thomas G. Dietterich: Editorial: New Editorial Board Members. Machine Learning 16(1-2): 5-6 (1994) | |
| 1993 | ||
| 37 | Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore: Memory-Based Methods for Regression and Classification. NIPS 1993: 1165-1166 | |
| 36 | Dietrich Wettschereck, Thomas G. Dietterich: Locally Adaptive Nearest Neighbor Algorithms. NIPS 1993: 184-191 | |
| 35 | 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 | |
| 34 | Thomas G. Dietterich: Editorial. Machine Learning 10: 5 (1993) | |
| 1992 | ||
| 33 | Hussein Almuallim, Thomas G. Dietterich: On Learning More Concepts. ML 1992: 11-19 | |
| 32 | Thomas G. Dietterich: Editorial. Machine Learning 8: 105 (1992) | |
| 1991 | ||
| 31 | Hussein Almuallim, Thomas G. Dietterich: Learning with Many Irrelevant Features. AAAI 1991: 547-552 | |
| 30 | Thomas G. Dietterich, Ghulum Bakiri: Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs. AAAI 1991: 572-577 | |
| 29 | Giuseppe Cerbone, Thomas G. Dietterich: Knowledge Compilation to Speed Up Numerical Optimisation. AI*IA 1991: 208-217 | |
| 28 | 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 | |
| 27 | Giuseppe Cerbone, Thomas G. Dietterich: Knowledge Compilation to Speed Up Numerical Optimization. ML 1991: 600-604 | |
| 26 | Dietrich Wettschereck, Thomas G. Dietterich: Improving the Performance of Radial Basis Function Networks by Learning Center Locations. NIPS 1991: 1133-1140 | |
| 25 | 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) | |
| 1990 | ||
| 24 | Howard E. Shrobe, Thomas G. Dietterich, William R. Swartout: Proceedings of the 8th National Conference on Artificial Intelligence. Boston, Massachusetts, July 29 - August 3, 1990, 2 Volumes. AAAI Press / The MIT Press 1990 | |
| 23 | Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri: A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping. ML 1990: 24-31 | |
| 22 | Thomas G. Dietterich: Exploratory Research in Machine Learning. Machine Learning 5: 5-9 (1990) | |
| 1989 | ||
| 21 | Ritchey A. Ruff, Thomas G. Dietterich: What Good Are Experiments?. ML 1989: 109-112 | |
| 20 | Thomas G. Dietterich: Limitations on Inductive Learning. ML 1989: 124-128 | |
| 19 | Thomas G. Dietterich: News and Notes. Machine Learning 3: 373-375 (1989) | |
| 18 | Thomas G. Dietterich: News and Notes. Machine Learning 4: 107-109 (1989) | |
| 17 | Nicholas S. Flann, Thomas G. Dietterich: A Study of Explanation-Based Methods for Inductive Learning. Machine Learning 4: 187-226 (1989) | |
| 1988 | ||
| 16 | Caroline N. Koff, Nicholas S. Flann, Thomas G. Dietterich: An Efficient ATMS for Equivalence Relations. AAAI 1988: 182-187 | |
| 15 | Thomas G. Dietterich: News and Notes. Machine Learning 3: 247-249 (1988) | |
| 1987 | ||
| 14 | Nicholas S. Flann, Thomas G. Dietterich, Dan R. Corpon: Forward Chaining Logic Programming with the ATMS. AAAI 1987: 24-29 | |
| 13 | Thomas G. Dietterich: News and Notes. Machine Learning 2(1): 75-96 (1987) | |
| 12 | Thomas G. Dietterich: News and Notes. Machine Learning 2(2): 191-192 (1987) | |
| 11 | Thomas G. Dietterich: News and Notes. Machine Learning 2(3): 277-278 (1987) | |
| 10 | Thomas G. Dietterich: News and Notes. Machine Learning 2(4): 397-398 (1987) | |
| 1986 | ||
| 9 | Nicholas S. Flann, Thomas G. Dietterich: Selecting Appropriate Representations for Learning from Examples. AAAI 1986: 460-466 | |
| 8 | Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins: News and Notes. Machine Learning 1(2): 227-242 (1986) | |
| 7 | Thomas G. Dietterich: Learning at the Knowledge Level. Machine Learning 1(3): 287-316 (1986) | |
| 6 | Yves Kodratoff, Gheorghe Tecuci, Thomas G. Dietterich: News and Notes. Machine Learning 1(3): 355-358 (1986) | |
| 5 | Thomas G. Dietterich: News and Notes. Machine Learning 1(4): 453-454 (1986) | |
| 1985 | ||
| 4 | Thomas G. Dietterich, Ryszard S. Michalski: Discovering Patterns in Sequences of Events. Artif. Intell. 25(2): 187-232 (1985) | |
| 1984 | ||
| 3 | Thomas G. Dietterich: Learning About Systems That Contain State Variables. AAAI 1984: 96-100 | |
| 1981 | ||
| 2 | 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 | ||
| 1 | Thomas G. Dietterich: Applying General Induction Methods to the Card Game Eleusis. AAAI 1980: 218-220 | |
Colors in the list of coauthors
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