 | 2009 |
|---|
| 56 |       | Gabriel Doyle,
Charles Elkan:
Accounting for burstiness in topic models.
ICML 2009: 36 |
| 55 | | Milton 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 |
|---|
| 54 | | Keith 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 |
| 53 | | Guilherme Hoefel,
Charles Elkan:
Learning a two-stage SVM/CRF sequence classifier.
CIKM 2008: 271-278 |
| 52 | | Charles Elkan,
Keith Noto:
Learning classifiers from only positive and unlabeled data.
KDD 2008: 213-220 |
| 2007 |
|---|
| 51 | | Andrew T. Smith,
Charles Elkan:
Making generative classifiers robust to selection bias.
KDD 2007: 657-666 |
| 50 | | Sanmay Das,
Milton H. Saier Jr.,
Charles Elkan:
Finding Transport Proteins in a General Protein Database.
PKDD 2007: 54-66 |
| 49 | | James Bennett,
Charles Elkan,
Bing Liu,
Padhraic Smyth,
Domonkos Tikk:
KDD Cup and workshop 2007.
SIGKDD Explorations 9(2): 51-52 (2007) |
| 2006 |
|---|
| 48 | | Charles Elkan:
Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution.
ICML 2006: 289-296 |
| 2005 |
|---|
| 47 | | Rasmus Elsborg Madsen,
David Kauchak,
Charles Elkan:
Modeling word burstiness using the Dirichlet distribution.
ICML 2005: 545-552 |
| 46 | | Charles Elkan:
Deriving TF-IDF as a Fisher Kernel.
SPIRE 2005: 295-300 |
| 45 | | Douglas Turnbull,
Charles Elkan:
Fast Recognition of Musical Genres Using RBF Networks.
IEEE Trans. Knowl. Data Eng. 17(4): 580-584 (2005) |
| 2004 |
|---|
| 44 | | Andrew T. Smith,
Charles Elkan:
A Bayesian network framework for reject inference.
KDD 2004: 286-295 |
| 43 | | David Kauchak,
Joseph Smarr,
Charles Elkan:
Sources of Success for Boosted Wrapper Induction.
Journal of Machine Learning Research 5: 499-527 (2004) |
| 2003 |
|---|
| 42 | | David 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 |
| 39 | | Greg Hamerly,
Charles Elkan:
Learning the k in k-means.
NIPS 2003 |
| 2002 |
|---|
| 38 | | Greg Hamerly,
Charles Elkan:
Alternatives to the k-means algorithm that find better clusterings.
CIKM 2002: 600-607 |
| 37 | | Bianca Zadrozny,
Charles Elkan:
Transforming classifier scores into accurate multiclass probability estimates.
KDD 2002: 694-699 |
| 2001 |
|---|
| 36 | | Charles 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 |
| 32 | | Bianca Zadrozny,
Charles Elkan:
Learning and making decisions when costs and probabilities are both unknown.
KDD 2001: 204-213 |
| 31 | | Charles Elkan:
Magical thinking in data mining: lessons from CoIL challenge 2000.
KDD 2001: 426-431 |
| 30 | | Charles Elkan:
Paradoxes of fuzzy logic, revisited.
Int. J. Approx. Reasoning 26(2): 153-155 (2001) |
| 2000 |
|---|
| 29 | | Charles Elkan:
Results of the KDD'99 Classifier Learning.
SIGKDD Explorations 1(2): 63-64 (2000) |
| 28 | | Charles Elkan:
KDD'99 Knowledge Discovery Contest.
SIGKDD Explorations 1(2): 78 (2000) |
| 27 | | Fredrik 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 |
| 20 | | Alberto 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) |
| 14 | | Charles Elkan:
The Paradoxical Success of Fuzzy Logic.
IEEE Expert 9(4): 3-8 (1994) |
| 13 | | Charles 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 |
| 6 | | Charles 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 |
| 3 | | Charles Elkan:
Logical Characterizations of Nonmonotonic TMSs.
MFCS 1989: 218-224 |
| 2 | | Charles 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 |
