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Thomas G. Dietterich: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Hussein Almuallim, Thomas G. Dietterich
    Learning with Many Irrelevant Features. [Citation Graph (3, 0)][DBLP]
    AAAI, 1991, pp:547-552 [Conf]
  2. Thomas G. Dietterich, Ryszard S. Michalski
    Discovering Patterns in Sequences of Events. [Citation Graph (2, 0)][DBLP]
    Artif. Intell., 1985, v:25, n:2, pp:187-232 [Journal]
  3. Hussein Almuallim, Thomas G. Dietterich
    Learning Boolean Concepts in the Presence of Many Irrelevant Features. [Citation Graph (1, 0)][DBLP]
    Artif. Intell., 1994, v:69, n:1-2, pp:279-305 [Journal]
  4. Nicholas S. Flann, Thomas G. Dietterich
    A Study of Explanation-Based Methods for Inductive Learning. [Citation Graph (1, 0)][DBLP]
    Machine Learning, 1989, v:4, n:, pp:187-226 [Journal]
  5. Thomas G. Dietterich
    Applying General Induction Methods to the Card Game Eleusis. [Citation Graph (0, 0)][DBLP]
    AAAI, 1980, pp:218-220 [Conf]
  6. Thomas G. Dietterich
    Learning About Systems That Contain State Variables. [Citation Graph (0, 0)][DBLP]
    AAAI, 1984, pp:96-100 [Conf]
  7. Thomas G. Dietterich, Ghulum Bakiri
    Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs. [Citation Graph (0, 0)][DBLP]
    AAAI, 1991, pp:572-577 [Conf]
  8. Nicholas S. Flann, Thomas G. Dietterich
    Selecting Appropriate Representations for Learning from Examples. [Citation Graph (0, 0)][DBLP]
    AAAI, 1986, pp:460-466 [Conf]
  9. Nicholas S. Flann, Thomas G. Dietterich, Dan R. Corpon
    Forward Chaining Logic Programming with the ATMS. [Citation Graph (0, 0)][DBLP]
    AAAI, 1987, pp:24-29 [Conf]
  10. Caroline N. Koff, Nicholas S. Flann, Thomas G. Dietterich
    An Efficient ATMS for Equivalence Relations. [Citation Graph (0, 0)][DBLP]
    AAAI, 1988, pp:182-187 [Conf]
  11. Simone Stumpf, Xinlong Bao, Anton N. Dragunov, Thomas G. Dietterich, Jonathan L. Herlocker, Kevin Johnsrude, Lida Li, Jianqiang Shen
    The TaskTracker System. [Citation Graph (0, 0)][DBLP]
    AAAI, 2005, pp:1712-1713 [Conf]
  12. Giuseppe Cerbone, Thomas G. Dietterich
    Knowledge Compilation to Speed Up Numerical Optimisation. [Citation Graph (0, 0)][DBLP]
    AI*IA, 1991, pp:208-217 [Conf]
  13. Thomas G. Dietterich
    The Divide-and-Conquer Manifesto. [Citation Graph (0, 0)][DBLP]
    ALT, 2000, pp:13-26 [Conf]
  14. 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. [Citation Graph (0, 0)][DBLP]
    CCIA, 2002, pp:269-281 [Conf]
  15. Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton
    05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. [Citation Graph (0, 0)][DBLP]
    Probabilistic, Logical and Relational Learning, 2005, pp:- [Conf]
  16. Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen Muggleton
    05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. [Citation Graph (0, 0)][DBLP]
    Probabilistic, Logical and Relational Learning, 2005, pp:- [Conf]
  17. Thomas G. Dietterich, Xin Wang
    Support Vectors for Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ECML, 2001, pp:600- [Conf]
  18. Hussein Almuallim, Thomas G. Dietterich
    On Learning More Concepts. [Citation Graph (0, 0)][DBLP]
    ML, 1992, pp:11-19 [Conf]
  19. 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. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:577-580 [Conf]
  20. Eric Chown, Thomas G. Dietterich
    A Divide and Conquer Approach to Learning from Prior Knowledge. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:143-150 [Conf]
  21. Giuseppe Cerbone, Thomas G. Dietterich
    Knowledge Compilation to Speed Up Numerical Optimization. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:600-604 [Conf]
  22. Thomas G. Dietterich
    Limitations on Inductive Learning. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:124-128 [Conf]
  23. Thomas G. Dietterich
    The MAXQ Method for Hierarchical Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:118-126 [Conf]
  24. Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov
    Training conditional random fields via gradient tree boosting. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  25. Thomas G. Dietterich, Dídac Busquets, Ramon López de Mántaras, Carles Sierra
    Action Refinement in Reinforcement Learning by Probability Smoothing. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:107-114 [Conf]
  26. Thomas G. Dietterich, Nicholas S. Flann
    Explanation-Based Learning and Reinforcement Learning: A Unified View. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:176-184 [Conf]
  27. Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri
    A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping. [Citation Graph (0, 0)][DBLP]
    ML, 1990, pp:24-31 [Conf]
  28. Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour
    Applying the Waek Learning Framework to Understand and Improve C4.5. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:96-104 [Conf]
  29. Eun Bae Kong, Thomas G. Dietterich
    Error-Correcting Output Coding Corrects Bias and Variance. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:313-321 [Conf]
  30. Dragos D. Margineantu, Thomas G. Dietterich
    Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:583-590 [Conf]
  31. Dragos D. Margineantu, Thomas G. Dietterich
    Pruning Adaptive Boosting. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:211-218 [Conf]
  32. Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar
    Learning first-order probabilistic models with combining rules. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:609-616 [Conf]
  33. Ritchey A. Ruff, Thomas G. Dietterich
    What Good Are Experiments?. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:109-112 [Conf]
  34. Pengcheng Wu, Thomas G. Dietterich
    Improving SVM accuracy by training on auxiliary data sources. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  35. Prasad Tadepalli, Thomas G. Dietterich
    Hierarchical Explanation-Based Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:358-366 [Conf]
  36. Giorgio Valentini, Thomas G. Dietterich
    Low Bias Bagged Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:752-759 [Conf]
  37. Xin Wang, Thomas G. Dietterich
    Model-based Policy Gradient Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:776-783 [Conf]
  38. Valentina Bayer Zubek, Thomas G. Dietterich
    Pruning Improves Heuristic Search for Cost-Sensitive Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:19-26 [Conf]
  39. Wei Zhang, Hongli Deng, Thomas G. Dietterich, Eric N. Mortensen
    A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions. [Citation Graph (0, 0)][DBLP]
    ICPR (1), 2006, pp:778-782 [Conf]
  40. Wei Zhang, Thomas G. Dietterich
    A Reinforcement Learning Approach to job-shop Scheduling. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1995, pp:1114-1120 [Conf]
  41. Jianqiang Shen, Lida Li, Thomas G. Dietterich
    Real-Time Detection of Task Switches of Desktop Users. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:2868-2873 [Conf]
  42. Xinlong Bao, Jonathan L. Herlocker, Thomas G. Dietterich
    Fewer clicks and less frustration: reducing the cost of reaching the right folder. [Citation Graph (0, 0)][DBLP]
    Intelligent User Interfaces, 2006, pp:178-185 [Conf]
  43. 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. [Citation Graph (0, 0)][DBLP]
    IUI, 2005, pp:75-82 [Conf]
  44. Jianqiang Shen, Lida Li, Thomas G. Dietterich, Jonathan L. Herlocker
    A hybrid learning system for recognizing user tasks from desktop activities and email messages. [Citation Graph (0, 0)][DBLP]
    Intelligent User Interfaces, 2006, pp:86-92 [Conf]
  45. Jianqiang Shen, Thomas G. Dietterich
    Active EM to reduce noise in activity recognition. [Citation Graph (0, 0)][DBLP]
    Intelligent User Interfaces, 2007, pp:132-140 [Conf]
  46. 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. [Citation Graph (0, 0)][DBLP]
    Intelligent User Interfaces, 2007, pp:82-91 [Conf]
  47. Tony Fountain, Thomas G. Dietterich, Bill Sudyka
    Mining IC test data to optimize VLSI testing. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:18-25 [Conf]
  48. Thomas G. Dietterich
    Ensemble Methods in Machine Learning. [Citation Graph (0, 0)][DBLP]
    Multiple Classifier Systems, 2000, pp:1-15 [Conf]
  49. Giorgio Valentini, Thomas G. Dietterich
    Bias-Variance Analysis and Ensembles of SVM. [Citation Graph (0, 0)][DBLP]
    Multiple Classifier Systems, 2002, pp:222-231 [Conf]
  50. Thomas G. Dietterich
    State Abstraction in MAXQ Hierarchical Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:994-1000 [Conf]
  51. Thomas G. Dietterich, Xin Wang
    Batch Value Function Approximation via Support Vectors. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1491-1498 [Conf]
  52. Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore
    Memory-Based Methods for Regression and Classification. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:1165-1166 [Conf]
  53. Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez
    A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:216-223 [Conf]
  54. Xin Wang, Thomas G. Dietterich
    Stabilizing Value Function Approximation with the BFBP Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1587-1594 [Conf]
  55. Dietrich Wettschereck, Thomas G. Dietterich
    Improving the Performance of Radial Basis Function Networks by Learning Center Locations. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:1133-1140 [Conf]
  56. Dietrich Wettschereck, Thomas G. Dietterich
    Locally Adaptive Nearest Neighbor Algorithms. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:184-191 [Conf]
  57. Wei Zhang, Thomas G. Dietterich
    High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:1024-1030 [Conf]
  58. Thomas G. Dietterich, Xin Wang
    Support Vectors for Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    PKDD, 2001, pp:492- [Conf]
  59. Valentina Bayer Zubek, Thomas G. Dietterich
    A POMDP Approximation Algorithm That Anticipates the Need to Observe. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2000, pp:521-532 [Conf]
  60. Thomas G. Dietterich
    An Overview of MAXQ Hierarchical Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    SARA, 2000, pp:26-44 [Conf]
  61. Thomas G. Dietterich
    Machine Learning for Sequential Data: A Review. [Citation Graph (0, 0)][DBLP]
    SSPR/SPR, 2002, pp:15-30 [Conf]
  62. N. Larios, Hongli Deng, Wei Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. A. Lytle, Ruiz Correa, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich
    Automated Insect Identification through Concatenated Histograms of Local Appearance Features. [Citation Graph (0, 0)][DBLP]
    WACV, 2007, pp:26- [Conf]
  63. Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez
    Solving the Multiple Instance Problem with Axis-Parallel Rectangles. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1997, v:89, n:1-2, pp:31-71 [Journal]
  64. Thomas G. Dietterich, Ryszard S. Michalski
    Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1981, v:16, n:3, pp:257-294 [Journal]
  65. Thomas G. Dietterich
    Machine-Learning Research. [Citation Graph (0, 0)][DBLP]
    AI Magazine, 1997, v:18, n:4, pp:97-136 [Journal]
  66. Thomas G. Dietterich, Ghulum Bakiri
    Solving Multiclass Learning Problems via Error-Correcting Output Codes [Citation Graph (0, 0)][DBLP]
    CoRR, 1995, v:0, n:, pp:- [Journal]
  67. Thomas G. Dietterich
    Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition [Citation Graph (0, 0)][DBLP]
    CoRR, 1999, v:0, n:, pp:- [Journal]
  68. Thomas G. Dietterich
    State Abstraction in MAXQ Hierarchical Reinforcement Learning [Citation Graph (0, 0)][DBLP]
    CoRR, 1999, v:0, n:, pp:- [Journal]
  69. Thomas G. Dietterich
    Overfitting and Undercomputing in Machine Learning. [Citation Graph (0, 0)][DBLP]
    ACM Comput. Surv., 1995, v:27, n:3, pp:326-327 [Journal]
  70. Thomas G. Dietterich
    Machine Learning. [Citation Graph (0, 0)][DBLP]
    ACM Comput. Surv., 1996, v:28, n:4es, pp:3- [Journal]
  71. Ashok K. Goel, Tom Bylander, B. Chandrasekaran, Thomas G. Dietterich, Richard M. Keller, Chris Tong
    Knowledge Compilation: A Symposium. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1991, v:6, n:2, pp:71-93 [Journal]
  72. Thomas G. Dietterich
    Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2000, v:13, n:, pp:227-303 [Journal]
  73. Thomas G. Dietterich, Ghulum Bakiri
    Solving Multiclass Learning Problems via Error-Correcting Output Codes. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1995, v:2, n:, pp:263-286 [Journal]
  74. 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. [Citation Graph (0, 0)][DBLP]
    Journal of Computer-Aided Molecular Design, 1994, v:8, n:6, pp:635-652 [Journal]
  75. Giorgio Valentini, Thomas G. Dietterich
    Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:725-775 [Journal]
  76. Thomas G. Dietterich
    An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2000, v:40, n:2, pp:139-157 [Journal]
  77. Thomas G. Dietterich
    Learning at the Knowledge Level. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1986, v:1, n:3, pp:287-316 [Journal]
  78. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1986, v:1, n:4, pp:453-454 [Journal]
  79. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1987, v:2, n:1, pp:75-96 [Journal]
  80. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1987, v:2, n:2, pp:191-192 [Journal]
  81. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1987, v:2, n:3, pp:277-278 [Journal]
  82. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1987, v:2, n:4, pp:397-398 [Journal]
  83. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1988, v:3, n:, pp:247-249 [Journal]
  84. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1989, v:3, n:, pp:373-375 [Journal]
  85. Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1989, v:4, n:, pp:107-109 [Journal]
  86. Thomas G. Dietterich
    Exploratory Research in Machine Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1990, v:5, n:, pp:5-9 [Journal]
  87. Thomas G. Dietterich
    Editorial. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1992, v:8, n:, pp:105- [Journal]
  88. Thomas G. Dietterich
    Editorial. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1993, v:10, n:, pp:5- [Journal]
  89. Thomas G. Dietterich
    Editorial: New Editorial Board Members. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:16, n:1-2, pp:5-6 [Journal]
  90. Thomas G. Dietterich
    Editorial. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:22, n:1-3, pp:5-6 [Journal]
  91. Thomas G. Dietterich, Nicholas S. Flann
    Explanation-Based Learning and Reinforcement Learning: A Unified View. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:28, n:2-3, pp:169-210 [Journal]
  92. Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1986, v:1, n:2, pp:227-242 [Journal]
  93. Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri
    A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1995, v:18, n:1, pp:51-80 [Journal]
  94. Yves Kodratoff, Gheorghe Tecuci, Thomas G. Dietterich
    News and Notes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1986, v:1, n:3, pp:355-358 [Journal]
  95. Dietrich Wettschereck, Thomas G. Dietterich
    An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1995, v:19, n:1, pp:5-27 [Journal]
  96. Thomas G. Dietterich
    Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:7, pp:1895-1923 [Journal]
  97. Thomas G. Dietterich
    Machine Learning in Ecosystem Informatics. [Citation Graph (0, 0)][DBLP]
    ALT, 2007, pp:10-11 [Conf]
  98. Hongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro
    Principal Curvature-Based Region Detector for Object Recognition. [Citation Graph (0, 0)][DBLP]
    CVPR, 2007, pp:- [Conf]
  99. Thomas G. Dietterich
    Machine Learning in Ecosystem Informatics. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2007, pp:9-25 [Conf]
  100. Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich
    Learning from Sparse Data by Exploiting Monotonicity Constraints. [Citation Graph (0, 0)][DBLP]
    UAI, 2005, pp:18-26 [Conf]
  101. Simone Stumpf, Margaret M. Burnett, Tom Dietterich
    Improving Intelligent Assistants for Desktop Activities. [Citation Graph (0, 0)][DBLP]
    Interaction Challenges for Intelligent Assistants, 2007, pp:119-121 [Conf]
  102. Valentina Bayer Zubek, Thomas G. Dietterich
    Integrating Learning from Examples into the Search for Diagnostic Policies. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2005, v:24, n:, pp:263-303 [Journal]

  103. Integrating Multiple Learning Components through Markov Logic. [Citation Graph (, )][DBLP]


  104. Reinforcement Learning Via Practice and Critique Advice. [Citation Graph (, )][DBLP]


  105. The life and times of files and information: a study of desktop provenance. [Citation Graph (, )][DBLP]


  106. Dictionary-free categorization of very similar objects via stacked evidence trees. [Citation Graph (, )][DBLP]


  107. 07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. [Citation Graph (, )][DBLP]


  108. Automatic discovery and transfer of MAXQ hierarchies. [Citation Graph (, )][DBLP]


  109. Learning non-redundant codebooks for classifying complex objects. [Citation Graph (, )][DBLP]


  110. Learning visual dictionaries and decision lists for object recognition. [Citation Graph (, )][DBLP]


  111. Machine Learning in Ecosystem Informatics and Sustainability. [Citation Graph (, )][DBLP]


  112. Discovering frequent work procedures from resource connections. [Citation Graph (, )][DBLP]


  113. Detecting and correcting user activity switches: algorithms and interfaces. [Citation Graph (, )][DBLP]


  114. Learning MDP Action Models Via Discrete Mixture Trees. [Citation Graph (, )][DBLP]


  115. A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. [Citation Graph (, )][DBLP]


  116. Machine Learning and Ecosystem Informatics: Challenges and Opportunities. [Citation Graph (, )][DBLP]


  117. Learning first-order probabilistic models with combining rules. [Citation Graph (, )][DBLP]


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