dblp.uni-trier.dewww.uni-trier.de

Thomas G. Dietterich Vis

List of publications from the DBLP Bibliography Server - FAQ
Coauthor Index - Ask others: ACM DL/Guide - CiteSeerX - CSB - MetaPress - Google - Bing - Yahoo
Home Page

*2009
120EEWei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich: Learning non-redundant codebooks for classifying complex objects. ICML 2009: 156
119EEThomas G. Dietterich: Machine Learning in Ecosystem Informatics and Sustainability. IJCAI 2009: 8-13
118EEJianqiang 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
117EEJianqiang Shen, Erin Fitzhenry, Thomas G. Dietterich: Discovering frequent work procedures from resource connections. IUI 2009: 277-286
116EEJianqiang Shen, Thomas G. Dietterich: A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments. SDM 2009: 164-172
115EESimone 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)
2008
114 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
113 Thomas G. Dietterich, Xinlong Bao: Integrating Multiple Learning Components through Markov Logic. AAAI 2008: 622-627
112EEMichael Wynkoop, Thomas G. Dietterich: Learning MDP Action Models Via Discrete Mixture Trees. ECML/PKDD (2) 2008: 597-612
111EENeville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich: Automatic discovery and transfer of MAXQ hierarchies. ICML 2008: 648-655
110EEWei Zhang, Thomas G. Dietterich: Learning visual dictionaries and decision lists for object recognition. ICPR 2008: 1-4
109EESriraam 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)
108EENatalia 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)
107EEThomas 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
106EEThomas G. Dietterich: Machine Learning in Ecosystem Informatics. ALT 2007: 10-11
105EEHongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro: Principal Curvature-Based Region Detector for Object Recognition. CVPR 2007
104EEThomas G. Dietterich: Machine Learning in Ecosystem Informatics. Discovery Science 2007: 9-25
103EEJianqiang Shen, Lida Li, Thomas G. Dietterich: Real-Time Detection of Task Switches of Desktop Users. IJCAI 2007: 2868-2873
102EEJianqiang Shen, Thomas G. Dietterich: Active EM to reduce noise in activity recognition. IUI 2007: 132-140
101EESimone 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
99EELuc 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
98EENatalia 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
96EEWei 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
95EEXinlong Bao, Jonathan L. Herlocker, Thomas G. Dietterich: Fewer clicks and less frustration: reducing the cost of reaching the right folder. IUI 2006: 178-185
94EEJianqiang 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
92EESriraam 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
91EEAnton 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
90EELuc 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
89EELuc 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
88EEEric Altendorf, Angelo C. Restificar, Thomas G. Dietterich: Learning from Sparse Data by Exploiting Monotonicity Constraints. UAI 2005: 18-26
87EEValentina 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
86EEPengcheng Wu, Thomas G. Dietterich: Improving SVM accuracy by training on auxiliary data sources. ICML 2004
85EEThomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov: Training conditional random fields via gradient tree boosting. ICML 2004
84EEGiorgio 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
81EEDídac 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
78EEGiorgio Valentini, Thomas G. Dietterich: Bias-Variance Analysis and Ensembles of SVM. Multiple Classifier Systems 2002: 222-231
77EEThomas 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
74EEThomas G. Dietterich, Xin Wang: Support Vectors for Reinforcement Learning. ECML 2001: 600
73EEThomas G. Dietterich, Xin Wang: Batch Value Function Approximation via Support Vectors. NIPS 2001: 1491-1498
72EEXin Wang, Thomas G. Dietterich: Stabilizing Value Function Approximation with the BFBP Algorithm. NIPS 2001: 1587-1594
71EEThomas G. Dietterich, Xin Wang: Support Vectors for Reinforcement Learning. PKDD 2001: 492
2000
70EEThomas 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
67EETony Fountain, Thomas G. Dietterich, Bill Sudyka: Mining IC test data to optimize VLSI testing. KDD 2000: 18-25
66EEThomas 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
64EEThomas G. Dietterich: An Overview of MAXQ Hierarchical Reinforcement Learning. SARA 2000: 26-44
63EEThomas 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
61EEThomas G. Dietterich: State Abstraction in MAXQ Hierarchical Reinforcement Learning. NIPS 1999: 994-1000
60EEThomas G. Dietterich: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition CoRR cs.LG/9905014: (1999)
59EEThomas 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)
53EEThomas 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
45EEWei 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)
43EEThomas 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
36EEThomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore: Memory-Based Methods for Regression and Classification. NIPS 1993: 1165-1166
35EEDietrich Wettschereck, Thomas G. Dietterich: Locally Adaptive Nearest Neighbor Algorithms. NIPS 1993: 184-191
34EEThomas 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
25EEDietrich Wettschereck, Thomas G. Dietterich: Improving the Performance of Radial Basis Function Networks by Learning Center Locations. NIPS 1991: 1133-1140
24EEAshok 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

Coauthor Index

1Hussein Almuallim [30] [32] [39]
2Eric Altendorf [88] [92]
3Adam Ashenfelter [85]
4Christopher G. Atkeson [36]
5Ghulum Bakiri [23] [29] [41] [42] [43]
6Xinlong Bao [93] [95] [113] [118]
7Barr E. Bauer [38]
8Suzanna Becker [75]
9Yaroslav Bulatov [85]
10Margaret M. Burnett [100] [101] [115]
11Dídac Busquets [80] [81]
12Tom Bylander [24]
13Fredric Carl [118]
14Giuseppe Cerbone [26] [28]
15B. Chandrasekaran (Balakrishnan Chandrasekaran) [24]
16David Chapman [38]
17Steve A. Chien [27]
18Eric Chown [69]
19Dan R. Corpon [14]
20Ruiz Correa [98]
21Roger E. Critchlow Jr. [38]
22Hongli Deng [96] [98] [105] [108]
23Pedro Domingos [107]
24Richard J. Doyle [27]
25Anton N. Dragunov [91] [93]
26Russell Drummond [101]
27Brian Falkenhainer [27]
28Alan Fern [92] [109]
29Xiaoli Fern [120]
30Erin Fitzhenry [117]
31Nicholas S. Flann [8] [9] [14] [16] [17] [48] [52]
32Tony Fountain [67]
33James Garrett [27]
34Lise Getoor [89] [90] [97] [99] [107] [114]
35Zoubin Ghahramani [75]
36Ashok K. Goel [24]
37Michael Goodman [118]
38Jonathan L. Herlocker [91] [93] [94] [95] [101] [115]
39Hermann Hild [23] [41]
40Jed Irvine [118]
41Ajay N. Jain [34] [38]
42Kevin Johnsrude [91] [93]
43Michael J. Kearns [51]
44Richard M. Keller [24]
45Kristian Kersting [99] [114]
46Brenton Kirschner [118]
47Yves Kodratoff [6]
48Caroline N. Koff [16]
49Stephen Kolibaba [118]
50Eun Bae Kong [47]
51Natalia Larios [98] [108]
52Richard H. Lathrop [34] [38] [53]
53Todd K. Leen [76]
54Lida Li [91] [93] [94] [101] [103] [115]
55Tomás Lozano-Pérez [34] [38] [53]
56Stephen C. Y. Lu [27]
57David A. Lytle [98] [108]
58Yishay Mansour [51]
59Ramon López de Mántaras [80] [81]
60Dragos D. Margineantu [56] [68]
61Matthew R. McLaughlin [91]
62Neville Mehta [111]
63Ryszard S. Michalski [2] [4]
64Andrew Moldenke [98] [108]
65Andrew W. Moore [36]
66Eric N. Mortensen [96] [98] [105] [108]
67Stephen Muggleton [89] [90] [97] [99] [107] [114]
68Sriraam Natarajan [92] [109]
69Robert Paasch [98] [108]
70Luc De Raedt [89] [90] [97] [99] [114]
71Vidya Rajaram [101] [115]
72Soumya Ray [111]
73Angelo C. Restificar [88] [92]
74Ritchey A. Ruff [21]
75Salvador Ruiz-Correa [108]
76Matt Sarpola [98] [108]
77Linda G. Shapiro [98] [105] [108]
78Jianqiang Shen [93] [94] [102] [103] [116] [117] [118]
79Carles Sierra [80] [81]
80Simone Stumpf [93] [100] [101] [115] [118]
81Bill Sudyka [67]
82Erin Sullivan [101] [115]
83Akshat Surve [120]
84Prasad Tadepalli [55] [92] [107] [109] [111]
85Gheorghe Tecuci [6]
86Chris Tong [24]
87Anh Tran [118]
88Volker Tresp [76]
89Giorgio Valentini [78] [83] [84]
90Xin Wang [71] [72] [73] [74] [82]
91Teresa A. Webster [38]
92Dietrich Wettschereck [25] [35] [36] [40]
93Bradley L. Whitehall [27]
94David C. Wilkins [8]
95Weng-Keen Wong [115]
96Pengcheng Wu [86]
97Michael Wynkoop [112]
98Jenny Yuen [98] [108]
99Wei Zhang [45] [46] [96] [98] [105] [108] [110] [120]
100Valentina Bayer Zubek [65] [79] [87]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)