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Note: Links lead to the DBLP on the Web. Bianca Zadrozny Alina Beygelzimer , John Langford , Bianca Zadrozny: Weighted One-Against-All. AAAI 2005 : 720-725 Markus Ettl , Bianca Zadrozny, Pawan Chowdhary , Naoki Abe : Business Performance Management System for CRM and Sales Execution. DEXA Workshops 2005 : 908-913 Saharon Rosset , Claudia Perlich , Bianca Zadrozny: Ranking-Based Evaluation of Regression Models. ICDM 2005 : 370-377 Wei Fan , Ian Davidson , Bianca Zadrozny, Philip S. Yu : An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias. ICDM 2005 : 605-608 John Langford , Bianca Zadrozny: Relating reinforcement learning performance to classification performance. ICML 2005 : 473-480 Alina Beygelzimer , Varsha Dani , Tom Hayes , John Langford , Bianca Zadrozny: Error limiting reductions between classification tasks. ICML 2005 : 49-56 Gary Weiss , Maytal Saar-Tsechansky , Bianca Zadrozny: Report on UBDM-05: Workshop on Utility-Based Data Mining. SIGKDD Explorations 7 (2): 145-147 (2005) Bianca Zadrozny: Learning and evaluating classifiers under sample selection bias. ICML 2004 Naoki Abe , Bianca Zadrozny, John Langford : An iterative method for multi-class cost-sensitive learning. KDD 2004 : 3-11 Bianca Zadrozny, John Langford , Naoki Abe : Cost-Sensitive Learning by Cost-Proportionate Example Weighting. ICDM 2003 : 435- Naoki Abe , Edwin P. D. Pednault , Haixun Wang , Bianca Zadrozny, Wei Fan , Chidanand Apté : Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing. ICDM 2002 : 3-10 Edwin P. D. Pednault , Naoki Abe , Bianca Zadrozny: Sequential cost-sensitive decision making with reinforcement learning. KDD 2002 : 259-268 Bianca Zadrozny, Charles Elkan : Transforming classifier scores into accurate multiclass probability estimates. KDD 2002 : 694-699 Bianca Zadrozny, Charles Elkan : Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. ICML 2001 : 609-616 Bianca Zadrozny, Charles Elkan : Learning and making decisions when costs and probabilities are both unknown. KDD 2001 : 204-213 1 [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 14 ] 2 [ 5 ] 3 [ 10 ] [ 15 ] 4 [ 14 ] 5 [ 10 ] 6 [ 12 ] 7 [ 1 ] [ 2 ] [ 3 ] 8 [ 14 ] 9 [ 5 ] [ 12 ] 10 [ 10 ] 11 [ 6 ] [ 7 ] [ 10 ] [ 11 ] [ 15 ] 12 [ 4 ] [ 5 ] 13 [ 13 ] 14 [ 13 ] 15 [ 9 ] 16 [ 5 ] 17 [ 9 ] 18 [ 12 ]  ©2006 Association for Computing Machinery |
