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Note: Links lead to the DBLP on the Web. Charles Elkan Rasmus Elsborg Madsen , David Kauchak , Charles Elkan: Modeling word burstiness using the Dirichlet distribution. ICML 2005 : 545-552 Charles Elkan: Deriving TF-IDF as a Fisher Kernel. SPIRE 2005 : 295-300 Douglas Turnbull , Charles Elkan: Fast Recognition of Musical Genres Using RBF Networks. IEEE Trans. Knowl. Data Eng. 17 (4): 580-584 (2005) Andrew Smith , Charles Elkan: A Bayesian network framework for reject inference. KDD 2004 : 286-295 David Kauchak , Joseph Smarr , Charles Elkan: Sources of Success for Boosted Wrapper Induction. Journal of Machine Learning Research 5 : 499-527 (2004) David Kauchak , Charles Elkan: Learning Rules to Improve a Machine Translation System. ECML 2003 : 205-216 Charles Elkan: Using the Triangle Inequality to Accelerate k-Means. ICML 2003 : 147-153 Eric Wiewiora , Garrison W. Cottrell , Charles Elkan: Principled Methods for Advising Reinforcement Learning Agents. ICML 2003 : 792-799 Greg Hamerly , Charles Elkan: Learning the k in k-means. NIPS 2003 Greg Hamerly , Charles Elkan: Alternatives to the k-means algorithm that find better clusterings. CIKM 2002 : 600-607 Bianca Zadrozny , Charles Elkan: Transforming classifier scores into accurate multiclass probability estimates. KDD 2002 : 694-699 Charles Elkan: Shared challenges in data mining and computational biology (abstract of invited talk). BIOKDD 2001 : 44 Greg Hamerly , Charles Elkan: Bayesian approaches to failure prediction for disk drives. ICML 2001 : 202-209 Bianca Zadrozny , Charles Elkan: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. ICML 2001 : 609-616 Charles Elkan: The Foundations of Cost-Sensitive Learning. IJCAI 2001 : 973-978 Bianca Zadrozny , Charles Elkan: Learning and making decisions when costs and probabilities are both unknown. KDD 2001 : 204-213 Charles Elkan: Magical thinking in data mining: lessons from CoIL challenge 2000. KDD 2001 : 426-431 Charles Elkan: Paradoxes of fuzzy logic, revisited. Int. J. Approx. Reasoning 26 (2): 153-155 (2001) Charles Elkan: Results of the KDD'99 Classifier Learning. SIGKDD Explorations 1 (2): 63-64 (2000) Charles Elkan: KDD'99 Knowledge Discovery Contest. SIGKDD Explorations 1 (2): 78 (2000) Fredrik Farnstrom , James Lewis , Charles Elkan: Scalability for Clustering Algorithms Revisited. SIGKDD Explorations 2 (1): 51-57 (2000) 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 Alvaro E. Monge , Charles Elkan: An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. DMKD 1997 : 0- 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) Karan Bhatia , Charles Elkan: LPMEME: A Statistical Method for Inductive Logic Programming. Canadian Conference on AI 1996 : 227-239 Charles Elkan: Reasoning about Unknown, Counterfactual, and Nondeterministic Actions in First-Order Logic. Canadian Conference on AI 1996 : 54-68 Alvaro E. Monge , Charles Elkan: The Field Matching Problem: Algorithms and Applications. KDD 1996 : 267-270 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) 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) Timothy L. Bailey , Charles Elkan: The Value of Prior Knowledge in Discovering Motifs with MEME. ISMB 1995 : 21-29 Timothy L. Bailey , Charles Elkan: Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. Machine Learning 21 (1-2): 51-80 (1995) Timothy L. Bailey , Charles Elkan: Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer. ISMB 1994 : 28-36 Alberto Maria Segre , Charles Elkan: A High-Performance Explanation-Based Learning Algorithm. Artif. Intell. 69 (1-2): 1-50 (1994) Charles Elkan: The Paradoxical Success of Fuzzy Logic. IEEE Expert 9 (4): 3-8 (1994) Charles Elkan: Elkan's Reply: The Paradoxical Controversy over Fuzzy Logic. IEEE Expert 9 (4): 47-49 (1994) Charles Elkan: The Paradoxical Success of Fuzzy Logic. AAAI 1993 : 698-703 Timothy L. Bailey , Charles Elkan: Estimating the Accuracy of Learned Concepts. IJCAI 1993 : 895-901 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) Russell Greiner , Charles Elkan: Measuring and Improving the Effectiveness of Representations. IJCAI 1991 : 518-524 Alberto Maria Segre , Charles Elkan, Alexander Russell : A Critical Look at Experimental Evaluations of EBL. Machine Learning 6 : 183-195 (1991) Charles Elkan: Incremental, Approximate Planning. AAAI 1990 : 145-150 Charles Elkan: Independence of Logic Database Queries and Updates. PODS 1990 : 154-160 Charles Elkan: A Rational Reconstruction of Nonmonotonic Truth Maintenance Systems. Artif. Intell. 43 (2): 219-234 (1990) Charles Elkan: Conspiracy Numbers and Caching for Searching And/Or Trees and Theorem-Proving. IJCAI 1989 : 341-348 Charles Elkan: Logical Characterizations of Nonmonotonic TMSs. MFCS 1989 : 218-224 Charles Elkan: A Decision Procedure for Conjunctive Query Disjointness. PODS 1989 : 134-139 Charles Elkan, David A. McAllester : Automated Inductive Reasoning about Logic Programs. ICLP/SLP 1988 : 876-892 1 [ 11 ] [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 24 ] [ 26 ] 2 [ 24 ] [ 26 ] 3 [ 23 ] 4 [ 40 ] 5 [ 27 ] 6 [ 20 ] 7 [ 9 ] [ 10 ] 8 [ 19 ] [ 24 ] [ 26 ] 9 [ 35 ] [ 38 ] [ 39 ] 10 [ 42 ] [ 43 ] [ 47 ] 11 [ 27 ] 12 [ 47 ] 13 [ 1 ] 14 [ 21 ] [ 25 ] 15 [ 8 ] 16 [ 8 ] [ 15 ] [ 20 ] 17 [ 43 ] 18 [ 44 ] 19 [ 45 ] 20 [ 40 ] 21 [ 32 ] [ 34 ] [ 37 ]  ©2006 Association for Computing Machinery |
