Charles Elkan

Ramón Huerta, Fernando J. Corbacho, Charles Elkan: Nonlinear support vector machines can systematically identify stocks with high and low future returns. Algorithmic Finance 2(1): 45-58 (2013)

Ramón Huerta, Shankar Vembu, José M. Amigó, Thomas Nowotny, Charles Elkan: Inhibition in Multiclass Classification. Neural Computation 24(9): 2473-2507 (2012)

Charles Elkan, Yehuda Koren: Guest Editorial for Special Issue KDD'10. TKDD 5(4): 18 (2012)

Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado: Predicting accurate probabilities with a ranking loss. ICML 2012

Vivek Ramavajjala, Charles Elkan: Policy Iteration Based on a Learned Transition Model. ECML/PKDD (2) 2012: 211-226

Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan: Learning and Inference in Probabilistic Classifier Chains with Beam Search. ECML/PKDD (1) 2012: 665-680

Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado: Predicting accurate probabilities with a ranking loss. CoRR abs/1206.4661 (2012)

Martin Krallinger, Miguel Vazquez, Florian Leitner, David Salgado, Andrew Chatr-aryamontri, Andrew G. Winter, Livia Perfetto, Leonardo Briganti, Luana Licata, Marta Iannuccelli, Luisa Castagnoli, Gianni Cesareni, Mike Tyers, Gerold Schneider, Fabio Rinaldi, Robert Leaman, Graciela Gonzalez, Sérgio Matos, Sun Kim, W. John Wilbur, Luis Rocha, Hagit Shatkay, Ashish V. Tendulkar, Shashank Agarwal, Feifan Liu, Xinglong Wang, Rafal Rak, Keith Noto, Charles Elkan, Zhiyong Lu: The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text. BMC Bioinformatics 12(S-8): S3 (2011)

Aditya Kumar Sehgal, Sanmay Das, Keith Noto, Milton H. Saier Jr., Charles Elkan: Identifying Relevant Data for a Biological Database: Handcrafted Rules versus Machine Learning. IEEE/ACM Trans. Comput. Biology Bioinform. 8(3): 851-857 (2011)

Wenkai Li, Qinghua Guo, Charles Elkan: A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data. IEEE T. Geoscience and Remote Sensing 49(2): 717-725 (2011)

Aditya Krishna Menon, Charles Elkan: Fast Algorithms for Approximating the Singular Value Decomposition. TKDD 5(2): 13 (2011)

Charles Elkan: Reinforcement Learning with a Bilinear Q Function. EWRL 2011: 78-88

Aditya Krishna Menon, Charles Elkan: Link Prediction via Matrix Factorization. ECML/PKDD (2) 2011: 437-452

Luigi Cerulo, Charles Elkan, Michele Ceccarelli: Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinformatics 11: 228 (2010)

Padhraic Smyth, Charles Elkan: Technical perspective - Creativity helps influence prediction precision. Commun. ACM 53(4): 88 (2010)

Aditya Krishna Menon, Charles Elkan: Predicting labels for dyadic data. Data Min. Knowl. Discov. 21(2): 327-343 (2010)

Irene Rodriguez-Lujan, Ramón Huerta, Charles Elkan, Carlos Santa Cruz: Quadratic Programming Feature Selection. Journal of Machine Learning Research 11: 1491-1516 (2010)

Avinash Atreya, Charles Elkan: Latent semantic indexing (LSI) fails for TREC collections. SIGKDD Explorations 12(2): 5-10 (2010)

Nikolaos Trogkanis, Charles Elkan: Conditional Random Fields for Word Hyphenation. ACL 2010: 366-374

Aditya Krishna Menon, Charles Elkan: A Log-Linear Model with Latent Features for Dyadic Prediction. ICDM 2010: 364-373

Charles Elkan: Preserving Privacy in Data Mining via Importance Weighting. PSDML 2010: 15-21

Aditya Krishna Menon, Charles Elkan: Dyadic Prediction Using a Latent Feature Log-Linear Model. CoRR abs/1006.2156 (2010)

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)

Gabriel Doyle, Charles Elkan: Accounting for burstiness in topic models. ICML 2009: 36

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

Guilherme Hoefel, Charles Elkan: Learning a two-stage SVM/CRF sequence classifier. CIKM 2008: 271-278

Charles Elkan, Keith Noto: Learning classifiers from only positive and unlabeled data. KDD 2008: 213-220

James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, Domonkos Tikk: KDD Cup and workshop 2007. SIGKDD Explorations 9(2): 51-52 (2007)

Andrew T. Smith, Charles Elkan: Making generative classifiers robust to selection bias. KDD 2007: 657-666

Sanmay Das, Milton H. Saier Jr., Charles Elkan: Finding Transport Proteins in a General Protein Database. PKDD 2007: 54-66

Charles Elkan: Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution. ICML 2006: 289-296

Douglas Turnbull, Charles Elkan: Fast Recognition of Musical Genres Using RBF Networks. IEEE Trans. Knowl. Data Eng. 17(4): 580-584 (2005)

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

David Kauchak, Joseph Smarr, Charles Elkan: Sources of Success for Boosted Wrapper Induction. Journal of Machine Learning Research 5: 499-527 (2004)

Andrew T. Smith, Charles Elkan: A Bayesian network framework for reject inference. KDD 2004: 286-295

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

Gordon F. Hughes, Joseph F. Murray, Kenneth Kreutz-Delgado, Charles Elkan: Improved disk-drive failure warnings. IEEE Transactions on Reliability 51(3): 350-357 (2002)

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: Paradoxes of fuzzy logic, revisited. Int. J. Approx. Reasoning 26(2): 153-155 (2001)

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

Charles Elkan: Shared challenges in data mining and computational biology (abstract of invited talk). BIOKDD 2001: 44

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: 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

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)

Alvaro E. Monge, Charles Elkan: An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. DMKD 1997: 0-

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)

Charles Elkan: Reasoning about Unknown, Counterfactual, and Nondeterministic Actions in First-Order Logic. Canadian Conference on AI 1996: 54-68

Karan Bhatia, Charles Elkan: LPMEME: A Statistical Method for Inductive Logic Programming. Canadian Conference on AI 1996: 227-239

Alvaro E. Monge, Charles Elkan: The Field Matching Problem: Algorithms and Applications. KDD 1996: 267-270

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: The Value of Prior Knowledge in Discovering Motifs with MEME. ISMB 1995: 21-29

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)

Timothy L. Bailey, Charles Elkan: Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer. ISMB 1994: 28-36

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)

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

Alberto Maria Segre, Charles Elkan, Alexander Russell: A Critical Look at Experimental Evaluations of EBL. Machine Learning 6: 183-195 (1991)

Russell Greiner, Charles Elkan: Measuring and Improving the Effectiveness of Representations. IJCAI 1991: 518-524

Charles Elkan: A Rational Reconstruction of Nonmonotonic Truth Maintenance Systems. Artif. Intell. 43(2): 219-234 (1990)

Charles Elkan: Incremental, Approximate Planning. AAAI 1990: 145-150

Charles Elkan: Independence of Logic Database Queries and Updates. PODS 1990: 154-160

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