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Charles Elkan Vis

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*2009
56EEGabriel Doyle, Charles Elkan: Accounting for burstiness in topic models. ICML 2009: 36
55EEMilton H. Saier Jr., Ming Ren Yen, Keith Noto, Dorjee G. Tamang, Charles Elkan: The Transporter Classification Database: recent advances. Nucleic Acids Research 37(Database-Issue): 274-278 (2009)
2008
54EEKeith Noto, Milton H. Saier Jr., Charles Elkan: Learning to Find Relevant Biological Articles without Negative Training Examples. Australasian Conference on Artificial Intelligence 2008: 202-213
53EEGuilherme Hoefel, Charles Elkan: Learning a two-stage SVM/CRF sequence classifier. CIKM 2008: 271-278
52EECharles Elkan, Keith Noto: Learning classifiers from only positive and unlabeled data. KDD 2008: 213-220
2007
51EEAndrew T. Smith, Charles Elkan: Making generative classifiers robust to selection bias. KDD 2007: 657-666
50EESanmay Das, Milton H. Saier Jr., Charles Elkan: Finding Transport Proteins in a General Protein Database. PKDD 2007: 54-66
49EEJames Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, Domonkos Tikk: KDD Cup and workshop 2007. SIGKDD Explorations 9(2): 51-52 (2007)
2006
48EECharles Elkan: Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution. ICML 2006: 289-296
2005
47EERasmus Elsborg Madsen, David Kauchak, Charles Elkan: Modeling word burstiness using the Dirichlet distribution. ICML 2005: 545-552
46EECharles Elkan: Deriving TF-IDF as a Fisher Kernel. SPIRE 2005: 295-300
45EEDouglas Turnbull, Charles Elkan: Fast Recognition of Musical Genres Using RBF Networks. IEEE Trans. Knowl. Data Eng. 17(4): 580-584 (2005)
2004
44EEAndrew T. Smith, Charles Elkan: A Bayesian network framework for reject inference. KDD 2004: 286-295
43EEDavid Kauchak, Joseph Smarr, Charles Elkan: Sources of Success for Boosted Wrapper Induction. Journal of Machine Learning Research 5: 499-527 (2004)
2003
42EEDavid Kauchak, Charles Elkan: Learning Rules to Improve a Machine Translation System. ECML 2003: 205-216
41 Charles Elkan: Using the Triangle Inequality to Accelerate k-Means. ICML 2003: 147-153
40 Eric Wiewiora, Garrison W. Cottrell, Charles Elkan: Principled Methods for Advising Reinforcement Learning Agents. ICML 2003: 792-799
39EEGreg Hamerly, Charles Elkan: Learning the k in k-means. NIPS 2003
2002
38EEGreg Hamerly, Charles Elkan: Alternatives to the k-means algorithm that find better clusterings. CIKM 2002: 600-607
37EEBianca Zadrozny, Charles Elkan: Transforming classifier scores into accurate multiclass probability estimates. KDD 2002: 694-699
2001
36EECharles Elkan: Shared challenges in data mining and computational biology (abstract of invited talk). BIOKDD 2001: 44
35 Greg Hamerly, Charles Elkan: Bayesian approaches to failure prediction for disk drives. ICML 2001: 202-209
34 Bianca Zadrozny, Charles Elkan: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. ICML 2001: 609-616
33 Charles Elkan: The Foundations of Cost-Sensitive Learning. IJCAI 2001: 973-978
32EEBianca Zadrozny, Charles Elkan: Learning and making decisions when costs and probabilities are both unknown. KDD 2001: 204-213
31EECharles Elkan: Magical thinking in data mining: lessons from CoIL challenge 2000. KDD 2001: 426-431
30EECharles Elkan: Paradoxes of fuzzy logic, revisited. Int. J. Approx. Reasoning 26(2): 153-155 (2001)
2000
29EECharles Elkan: Results of the KDD'99 Classifier Learning. SIGKDD Explorations 1(2): 63-64 (2000)
28EECharles Elkan: KDD'99 Knowledge Discovery Contest. SIGKDD Explorations 1(2): 78 (2000)
27EEFredrik Farnstrom, James Lewis, Charles Elkan: Scalability for Clustering Algorithms Revisited. SIGKDD Explorations 2(1): 51-57 (2000)
1999
26 Timothy L. Bailey, Michael E. Baker, Charles Elkan, William Noble Grundy: MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences. Pattern Discovery in Biomolecular Data 1999: 30-54
1997
25 Alvaro E. Monge, Charles Elkan: An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. DMKD 1997: 0-
24 William Noble Grundy, Timothy L. Bailey, Charles Elkan, Michael E. Baker: Meta-MEME: motif-based hidden Markov models of protein families. Computer Applications in the Biosciences 13(4): 397-406 (1997)
1996
23 Karan Bhatia, Charles Elkan: LPMEME: A Statistical Method for Inductive Logic Programming. Canadian Conference on AI 1996: 227-239
22 Charles Elkan: Reasoning about Unknown, Counterfactual, and Nondeterministic Actions in First-Order Logic. Canadian Conference on AI 1996: 54-68
21 Alvaro E. Monge, Charles Elkan: The Field Matching Problem: Algorithms and Applications. KDD 1996: 267-270
20EEAlberto Maria Segre, Geoffrey J. Gordon, Charles Elkan: Exploratory Analysis of Speedup Learning Data Using Epectation Maximization. Artif. Intell. 85(1-2): 301-319 (1996)
19 William Noble Grundy, Timothy L. Bailey, Charles Elkan: ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool. Computer Applications in the Biosciences 12(4): 303-310 (1996)
1995
18 Timothy L. Bailey, Charles Elkan: The Value of Prior Knowledge in Discovering Motifs with MEME. ISMB 1995: 21-29
17 Timothy L. Bailey, Charles Elkan: Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. Machine Learning 21(1-2): 51-80 (1995)
1994
16 Timothy L. Bailey, Charles Elkan: Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer. ISMB 1994: 28-36
15 Alberto Maria Segre, Charles Elkan: A High-Performance Explanation-Based Learning Algorithm. Artif. Intell. 69(1-2): 1-50 (1994)
14EECharles Elkan: The Paradoxical Success of Fuzzy Logic. IEEE Expert 9(4): 3-8 (1994)
13EECharles Elkan: Elkan's Reply: The Paradoxical Controversy over Fuzzy Logic. IEEE Expert 9(4): 47-49 (1994)
1993
12 Charles Elkan: The Paradoxical Success of Fuzzy Logic. AAAI 1993: 698-703
11 Timothy L. Bailey, Charles Elkan: Estimating the Accuracy of Learned Concepts. IJCAI 1993: 895-901
10 Charles Elkan, Russell Greiner: D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Artif. Intell. 61(1): 41-52 (1993)
1991
9 Russell Greiner, Charles Elkan: Measuring and Improving the Effectiveness of Representations. IJCAI 1991: 518-524
8 Alberto Maria Segre, Charles Elkan, Alexander Russell: A Critical Look at Experimental Evaluations of EBL. Machine Learning 6: 183-195 (1991)
1990
7 Charles Elkan: Incremental, Approximate Planning. AAAI 1990: 145-150
6EECharles Elkan: Independence of Logic Database Queries and Updates. PODS 1990: 154-160
5 Charles Elkan: A Rational Reconstruction of Nonmonotonic Truth Maintenance Systems. Artif. Intell. 43(2): 219-234 (1990)
1989
4 Charles Elkan: Conspiracy Numbers and Caching for Searching And/Or Trees and Theorem-Proving. IJCAI 1989: 341-348
3EECharles Elkan: Logical Characterizations of Nonmonotonic TMSs. MFCS 1989: 218-224
2EECharles Elkan: A Decision Procedure for Conjunctive Query Disjointness. PODS 1989: 134-139
1988
1 Charles Elkan, David A. McAllester: Automated Inductive Reasoning about Logic Programs. ICLP/SLP 1988: 876-892

Coauthor Index

1Timothy L. Bailey [11] [16] [17] [18] [19] [24] [26]
2Michael E. Baker [24] [26]
3James Bennett [49]
4Karan Bhatia [23]
5Garrison W. Cottrell [40]
6Sanmay Das [50]
7Gabriel Doyle [56]
8Fredrik Farnstrom [27]
9Geoffrey J. Gordon [20]
10Russell Greiner [9] [10]
11William Noble Grundy [19] [24] [26]
12Greg Hamerly [35] [38] [39]
13Guilherme Hoefel [53]
14David Kauchak [42] [43] [47]
15James Lewis [27]
16Bing Liu [49]
17Rasmus Elsborg Madsen [47]
18David A. McAllester [1]
19Alvaro E. Monge [21] [25]
20Keith Noto [52] [54] [55]
21Alexander Russell [8]
22Milton H. Saier Jr. [50] [54] [55]
23Alberto Maria Segre [8] [15] [20]
24Joseph Smarr [43]
25Andrew T. Smith [44] [51]
26Padhraic Smyth [49]
27Dorjee G. Tamang [55]
28Domonkos Tikk [49]
29Douglas Turnbull [45]
30Eric Wiewiora [40]
31Ming Ren Yen [55]
32Bianca Zadrozny [32] [34] [37]

Colors in the list of coauthors

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