Travelled to:1 × Germany
1 × United Kingdom
13 × USA
2 × Canada
Collaborated with:∅ B.Zadrozny A.T.Smith G.Hamerly G.Doyle G.Hoefel K.Noto A.E.Monge D.A.McAllester R.E.Madsen D.Kauchak E.Wiewiora G.W.Cottrell A.K.Menon X.Jiang S.Vembu L.Ohno-Machado
Talks about:classifi (5) probabl (4) learn (4) bayesian (3) decis (3) distribut (2) dirichlet (2) algorithm (2) predict (2) cluster (2)
Person: Charles Elkan
DBLP: Elkan:Charles
Contributed to:
20122009
2008
20082007200620052004200320022002200120011996
19901989
1988Wrote 20 papers:
- ICML-2012-MenonJVEO #predict #ranking
- Predicting accurate probabilities with a ranking loss (AKM, XJ, SV, CE, LOM), p. 88.
- ICML-2009-DoyleE #modelling #topic
- Accounting for burstiness in topic models (GD, CE), pp. 281–288.
- CIKM-2008-HoefelE #classification #learning #sequence
- Learning a two-stage SVM/CRF sequence classifier (GH, CE), pp. 271–278.
- KDD-2008-ElkanN #classification #learning
- Learning classifiers from only positive and unlabeled data (CE, KN), pp. 213–220.
- KDD-2007-SmithE #bias #classification #generative #robust
- Making generative classifiers robust to selection bias (ATS, CE), pp. 657–666.
- ICML-2006-Elkan #approximate #clustering #documentation #multi
- Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution (CE), pp. 289–296.
- ICML-2005-MadsenKE #modelling #using #word
- Modeling word burstiness using the Dirichlet distribution (REM, DK, CE), pp. 545–552.
- KDD-2004-SmithE #framework #network
- A Bayesian network framework for reject inference (ATS, CE), pp. 286–295.
- ICML-2003-Elkan #difference #using
- Using the Triangle Inequality to Accelerate k-Means (CE), pp. 147–153.
- ICML-2003-WiewioraCE #learning
- Principled Methods for Advising Reinforcement Learning Agents (EW, GWC, CE), pp. 792–799.
- CIKM-2002-HamerlyE #algorithm #clustering
- Alternatives to the k-means algorithm that find better clusterings (GH, CE), pp. 600–607.
- KDD-2002-ZadroznyE #classification #multi #probability
- Transforming classifier scores into accurate multiclass probability estimates (BZ, CE), pp. 694–699.
- ICML-2001-HamerlyE #predict
- Bayesian approaches to failure prediction for disk drives (GH, CE), pp. 202–209.
- ICML-2001-ZadroznyE #classification #naive bayes #probability
- Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (BZ, CE), pp. 609–616.
- KDD-2001-Elkan #challenge #data mining #lessons learnt #mining
- Magical thinking in data mining: lessons from CoIL challenge 2000 (CE), pp. 426–431.
- KDD-2001-ZadroznyE #learning
- Learning and making decisions when costs and probabilities are both unknown (BZ, CE), pp. 204–213.
- KDD-1996-MongeE #algorithm #problem
- The Field Matching Problem: Algorithms and Applications (AEM, CE), pp. 267–270.
- PODS-1990-Elkan #database #independence #logic #query
- Independence of Logic Database Queries and Updates (CE), pp. 154–160.
- PODS-1989-Elkan #query
- A Decision Procedure for Conjunctive Query Disjointness (CE), pp. 134–139.
- JICSCP-1988-ElkanM88 #automation #induction #logic programming #reasoning #source code
- Automated Inductive Reasoning about Logic Programs (CE, DAM), pp. 876–892.