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Note: Links lead to the DBLP on the Web. Foster J. Provost 27 Maytal Saar-Tsechansky , Foster J. Provost: Active Learning for Class Probability Estimation and Ranking. IJCAI 2001 : 911-920 26 Sofus A. Macskassy , Haym Hirsh , Foster J. Provost, Ramesh Sankaranarayanan , Vasant Dhar : Intelligent Information Triage. SIGIR 2001 : 318-326 25 Ron Kohavi , Foster J. Provost: Applications of Data Mining to Electronic Commerce. Data Mining and Knowledge Discovery 5 (1/2): 5-10 (2001) 24 Foster J. Provost, Tom Fawcett : Robust Classification for Imprecise Environments. Machine Learning 42 (3): 203-231 (2001) 23 Vasant Dhar , Dashin Chou , Foster J. Provost: Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid with Entropy Reduction. Data Mining and Knowledge Discovery 4 (4): 251-280 (2000) 22 Foster J. Provost, David Jensen , Tim Oates : Efficient Progressive Sampling. KDD 1999 : 23-32 21 Tom Fawcett , Foster J. Provost: Activity Monitoring: Noticing Interesting Changes in Behavior. KDD 1999 : 53-62 20 Foster J. Provost, Venkateswarlu Kolluri : A Survey of Methods for Scaling Up Inductive Algorithms. Data Mining and Knowledge Discovery 3 (2): 131-169 (1999) 19 Foster J. Provost, Andrea Pohoreckyj Danyluk : Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. Informatica (Slovenia) 23 (1): (1999) 18 Foster J. Provost, Tom Fawcett : Robust Classification Systems for Imprecise Environments. AAAI/IAAI 1998 : 706-713 17 Tom Fawcett , Ira J. Haimowitz , Foster J. Provost, Salvatore J. Stolfo : AI Approaches to Fraud Detection and Risk Management. AI Magazine 19 (2): 107-108 (1998) 16 Foster J. Provost, Ron Kohavi : Guest Editors' Introduction: On Applied Research in Machine Learning. Machine Learning 30 (2-3): 127-132 (1998) 15 John M. Aronis , Foster J. Provost: Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. KDD 1997 : 119-122 14 Foster J. Provost, Venkateswarlu Kolluri : Scaling Up Inductive Algorithms: An Overview. KDD 1997 : 239-242 13 Foster J. Provost, Tom Fawcett : Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD 1997 : 43-48 12 Tom Fawcett , Foster J. Provost: Adaptive Fraud Detection. Data Mining and Knowledge Discovery 1 (3): 291-316 (1997) 11 Foster J. Provost, Daniel N. Hennessy : Scaling Up: Distributed Machine Learning with Cooperation. AAAI/IAAI, Vol. 1 1996 : 74-79 10 John M. Aronis , Foster J. Provost, Bruce G. Buchanan : Exploiting Background Knowledge in Automated Discovery. KDD 1996 : 355-358 9 Tom Fawcett , Foster J. Provost: Combining Data Mining and Machine Learning for Effective User Profiling. KDD 1996 : 8-13 8 Foster J. Provost, John M. Aronis : Scaling Up Inductive Learning with Massive Parallelism. Machine Learning 23 (1): 33-46 (1996) 7 Foster J. Provost, Bruce G. Buchanan : Inductive Policy: The Pragmatics of Bias Selection. Machine Learning 20 (1-2): 35-61 (1995) 6 John M. Aronis , Foster J. Provost: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. KDD Workshop 1994 : 347-358 5 Foster J. Provost: Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. AAAI 1993 : 749-755 4 Andrea Pohoreckyj Danyluk , Foster J. Provost: Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network. ICML 1993 : 81-88 3 Foster J. Provost, Bruce G. Buchanan : Inductive Policy. AAAI 1992 : 255-261 2 Foster J. Provost, Bruce G. Buchanan : Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search. AII 1992 : 294-304 1 Foster J. Provost: ClimBS: Searching the Bias Space. ICTAI 1992 : 146-153  DiSC'02 © 2003 Association for Computing Machinery |
