| 2009 |
|---|
| 115 | EE | 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 |
| 114 | EE | Jianqiang Shen,
Erin Fitzhenry,
Thomas G. Dietterich:
Discovering frequent work procedures from resource connections.
IUI 2009: 277-286 |
| 2008 |
|---|
| 113 | | 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 |
| 112 | | Thomas G. Dietterich,
Xinlong Bao:
Integrating Multiple Learning Components through Markov Logic.
AAAI 2008: 622-627 |
| 111 | EE | Michael Wynkoop,
Thomas G. Dietterich:
Learning MDP Action Models Via Discrete Mixture Trees.
ECML/PKDD (2) 2008: 597-612 |
| 110 | EE | Neville Mehta,
Soumya Ray,
Prasad Tadepalli,
Thomas G. Dietterich:
Automatic discovery and transfer of MAXQ hierarchies.
ICML 2008: 648-655 |
| 109 | EE | Wei Zhang,
Thomas G. Dietterich:
Learning visual dictionaries and decision lists for object recognition.
ICPR 2008: 1-4 |
| 108 | EE | 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) |
| 107 | EE | 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 |
|---|
| 106 | EE | Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics.
ALT 2007: 10-11 |
| 105 | EE | Hongli Deng,
Wei Zhang,
Eric N. Mortensen,
Thomas G. Dietterich,
Linda G. Shapiro:
Principal Curvature-Based Region Detector for Object Recognition.
CVPR 2007 |
| 104 | EE | Thomas G. Dietterich:
Machine Learning in Ecosystem Informatics.
Discovery Science 2007: 9-25 |
| 103 | EE | Jianqiang Shen,
Lida Li,
Thomas G. Dietterich:
Real-Time Detection of Task Switches of Desktop Users.
IJCAI 2007: 2868-2873 |
| 102 | EE | Jianqiang Shen,
Thomas G. Dietterich:
Active EM to reduce noise in activity recognition.
IUI 2007: 132-140 |
| 101 | EE | 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 |
| 100 | | Simone Stumpf,
Margaret M. Burnett,
Thomas G. Dietterich:
Improving Intelligent Assistants for Desktop Activities.
Interaction Challenges for Intelligent Assistants 2007: 119-121 |
| 99 | EE | 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 |
| 98 | EE | 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 |
|---|
| 97 | | 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 |
| 96 | EE | 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 |
| 95 | EE | 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 |
| 94 | EE | 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 |
|---|
| 93 | | 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 |
| 92 | EE | 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 |
| 91 | EE | 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 |
| 90 | EE | 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 |
| 89 | EE | 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 |
| 88 | EE | Eric Altendorf,
Angelo C. Restificar,
Thomas G. Dietterich:
Learning from Sparse Data by Exploiting Monotonicity Constraints.
UAI 2005: 18-26 |
| 87 | EE | 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 |
|---|
| 86 | EE | Pengcheng Wu,
Thomas G. Dietterich:
Improving SVM accuracy by training on auxiliary data sources.
ICML 2004 |
| 85 | EE | Thomas G. Dietterich,
Adam Ashenfelter,
Yaroslav Bulatov:
Training conditional random fields via gradient tree boosting.
ICML 2004 |
| 84 | EE | 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 |
|---|
| 83 | | Giorgio Valentini,
Thomas G. Dietterich:
Low Bias Bagged Support Vector Machines.
ICML 2003: 752-759 |
| 82 | | Xin Wang,
Thomas G. Dietterich:
Model-based Policy Gradient Reinforcement Learning.
ICML 2003: 776-783 |
| 2002 |
|---|
| 81 | EE | 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 |
| 80 | | Thomas G. Dietterich,
Dídac Busquets,
Ramon López de Mántaras,
Carles Sierra:
Action Refinement in Reinforcement Learning by Probability Smoothing.
ICML 2002: 107-114 |
| 79 | | Valentina Bayer Zubek,
Thomas G. Dietterich:
Pruning Improves Heuristic Search for Cost-Sensitive Learning.
ICML 2002: 19-26 |
| 78 | EE | Giorgio Valentini,
Thomas G. Dietterich:
Bias-Variance Analysis and Ensembles of SVM.
Multiple Classifier Systems 2002: 222-231 |
| 77 | EE | Thomas G. Dietterich:
Machine Learning for Sequential Data: A Review.
SSPR/SPR 2002: 15-30 |
| 2001 |
|---|
| 76 | | 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 |
| 75 | | 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 |
| 74 | EE | Thomas G. Dietterich,
Xin Wang:
Support Vectors for Reinforcement Learning.
ECML 2001: 600 |
| 73 | EE | Thomas G. Dietterich,
Xin Wang:
Batch Value Function Approximation via Support Vectors.
NIPS 2001: 1491-1498 |
| 72 | EE | Xin Wang,
Thomas G. Dietterich:
Stabilizing Value Function Approximation with the BFBP Algorithm.
NIPS 2001: 1587-1594 |
| 71 | EE | Thomas G. Dietterich,
Xin Wang:
Support Vectors for Reinforcement Learning.
PKDD 2001: 492 |
| 2000 |
|---|
| 70 | EE | Thomas G. Dietterich:
The Divide-and-Conquer Manifesto.
ALT 2000: 13-26 |
| 69 | | Eric Chown,
Thomas G. Dietterich:
A Divide and Conquer Approach to Learning from Prior Knowledge.
ICML 2000: 143-150 |
| 68 | | Dragos D. Margineantu,
Thomas G. Dietterich:
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers.
ICML 2000: 583-590 |
| 67 | EE | Tony Fountain,
Thomas G. Dietterich,
Bill Sudyka:
Mining IC test data to optimize VLSI testing.
KDD 2000: 18-25 |
| 66 | EE | Thomas G. Dietterich:
Ensemble Methods in Machine Learning.
Multiple Classifier Systems 2000: 1-15 |
| 65 | | Valentina Bayer Zubek,
Thomas G. Dietterich:
A POMDP Approximation Algorithm That Anticipates the Need to Observe.
PRICAI 2000: 521-532 |
| 64 | EE | Thomas G. Dietterich:
An Overview of MAXQ Hierarchical Reinforcement Learning.
SARA 2000: 26-44 |
| 63 | EE | Thomas G. Dietterich:
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition.
J. Artif. Intell. Res. (JAIR) 13: 227-303 (2000) |
| 62 | | 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 |
|---|
| 61 | EE | Thomas G. Dietterich:
State Abstraction in MAXQ Hierarchical Reinforcement Learning.
NIPS 1999: 994-1000 |
| 60 | EE | Thomas G. Dietterich:
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
CoRR cs.LG/9905014: (1999) |
| 59 | EE | Thomas G. Dietterich:
State Abstraction in MAXQ Hierarchical Reinforcement Learning
CoRR cs.LG/9905015: (1999) |
| 1998 |
|---|
| 58 | | Thomas G. Dietterich:
The MAXQ Method for Hierarchical Reinforcement Learning.
ICML 1998: 118-126 |
| 57 | | Thomas G. Dietterich:
Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms.
Neural Computation 10(7): 1895-1923 (1998) |
| 1997 |
|---|
| 56 | | Dragos D. Margineantu,
Thomas G. Dietterich:
Pruning Adaptive Boosting.
ICML 1997: 211-218 |
| 55 | | Prasad Tadepalli,
Thomas G. Dietterich:
Hierarchical Explanation-Based Reinforcement Learning.
ICML 1997: 358-366 |
| 54 | | Thomas G. Dietterich:
Machine-Learning Research.
AI Magazine 18(4): 97-136 (1997) |
| 53 | EE | 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) |
| 52 | | Thomas G. Dietterich,
Nicholas S. Flann:
Explanation-Based Learning and Reinforcement Learning: A Unified View.
Machine Learning 28(2-3): 169-210 (1997) |
| 1996 |
|---|
| 51 | | Thomas G. Dietterich,
Michael J. Kearns,
Yishay Mansour:
Applying the Waek Learning Framework to Understand and Improve C4.5.
ICML 1996: 96-104 |
| 50 | | Thomas G. Dietterich:
Machine Learning.
ACM Comput. Surv. 28(4es): 3 (1996) |
| 49 | | Thomas G. Dietterich:
Editorial.
Machine Learning 22(1-3): 5-6 (1996) |
| 1995 |
|---|
| 48 | | Thomas G. Dietterich,
Nicholas S. Flann:
Explanation-Based Learning and Reinforcement Learning: A Unified View.
ICML 1995: 176-184 |
| 47 | | Eun Bae Kong,
Thomas G. Dietterich:
Error-Correcting Output Coding Corrects Bias and Variance.
ICML 1995: 313-321 |
| 46 | | Wei Zhang,
Thomas G. Dietterich:
A Reinforcement Learning Approach to job-shop Scheduling.
IJCAI 1995: 1114-1120 |
| 45 | EE | Wei Zhang,
Thomas G. Dietterich:
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network.
NIPS 1995: 1024-1030 |
| 44 | | Thomas G. Dietterich:
Overfitting and Undercomputing in Machine Learning.
ACM Comput. Surv. 27(3): 326-327 (1995) |
| 43 | EE | Thomas G. Dietterich,
Ghulum Bakiri:
Solving Multiclass Learning Problems via Error-Correcting Output Codes
CoRR cs.AI/9501101: (1995) |
| 42 | | Thomas G. Dietterich,
Ghulum Bakiri:
Solving Multiclass Learning Problems via Error-Correcting Output Codes.
J. Artif. Intell. Res. (JAIR) 2: 263-286 (1995) |
| 41 | | 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) |
| 40 | | Dietrich Wettschereck,
Thomas G. Dietterich:
An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms.
Machine Learning 19(1): 5-27 (1995) |
| 1994 |
|---|
| 39 | | Hussein Almuallim,
Thomas G. Dietterich:
Learning Boolean Concepts in the Presence of Many Irrelevant Features.
Artif. Intell. 69(1-2): 279-305 (1994) |
| 38 | | 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) |
| 37 | | Thomas G. Dietterich:
Editorial: New Editorial Board Members.
Machine Learning 16(1-2): 5-6 (1994) |
| 1993 |
|---|
| 36 | EE | Thomas G. Dietterich,
Dietrich Wettschereck,
Christopher G. Atkeson,
Andrew W. Moore:
Memory-Based Methods for Regression and Classification.
NIPS 1993: 1165-1166 |
| 35 | EE | Dietrich Wettschereck,
Thomas G. Dietterich:
Locally Adaptive Nearest Neighbor Algorithms.
NIPS 1993: 184-191 |
| 34 | EE | 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 |
| 33 | | Thomas G. Dietterich:
Editorial.
Machine Learning 10: 5 (1993) |
| 1992 |
|---|
| 32 | | Hussein Almuallim,
Thomas G. Dietterich:
On Learning More Concepts.
ML 1992: 11-19 |
| 31 | | Thomas G. Dietterich:
Editorial.
Machine Learning 8: 105 (1992) |
| 1991 |
|---|
| 30 | | Hussein Almuallim,
Thomas G. Dietterich:
Learning with Many Irrelevant Features.
AAAI 1991: 547-552 |
| 29 | | Thomas G. Dietterich,
Ghulum Bakiri:
Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs.
AAAI 1991: 572-577 |
| 28 | | Giuseppe Cerbone,
Thomas G. Dietterich:
Knowledge Compilation to Speed Up Numerical Optimisation.
AI*IA 1991: 208-217 |
| 27 | | 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 |
| 26 | | Giuseppe Cerbone,
Thomas G. Dietterich:
Knowledge Compilation to Speed Up Numerical Optimization.
ML 1991: 600-604 |
| 25 | EE | Dietrich Wettschereck,
Thomas G. Dietterich:
Improving the Performance of Radial Basis Function Networks by Learning Center Locations.
NIPS 1991: 1133-1140 |
| 24 | EE | 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 |
|---|
| 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 |
