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Note: Links lead to the DBLP on the Web. Tom Fawcett 12 Tom Fawcett: Using Rule Sets to Maximize ROC Performance. ICDM 2001 : 131-138 11 Foster J. Provost , Tom Fawcett: Robust Classification for Imprecise Environments. Machine Learning 42 (3): 203-231 (2001) 10 Tom Fawcett, Foster J. Provost : Activity Monitoring: Noticing Interesting Changes in Behavior. KDD 1999 : 53-62 9 Foster J. Provost , Tom Fawcett: Robust Classification Systems for Imprecise Environments. AAAI/IAAI 1998 : 706-713 8 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) 7 Foster J. Provost , Tom Fawcett: Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD 1997 : 43-48 6 Tom Fawcett, Foster J. Provost : Adaptive Fraud Detection. Data Mining and Knowledge Discovery 1 (3): 291-316 (1997) 5 Tom Fawcett, Foster J. Provost : Combining Data Mining and Machine Learning for Effective User Profiling. KDD 1996 : 8-13 4 Tom Fawcett: Knowledge-Based Feature Discovery for Evaluation Functions. Computational Intelligence 12 : 42-64 (1996) 3 James P. Callan , Tom Fawcett, Edwina L. Rissland : CABOT: An Adaptive Approach to Case-Based Search. IJCAI 1991 : 803-809 2 Tom Fawcett, Paul E. Utgoff : A Hybrid Method for Feature Generation. ML 1991 : 137-141 1 John Vittal , Bernard Silver , William J. Frawley , Glenn A. Iba , Tom Fawcett, Susan Dusseault , John Doleac : A Framework for Cooperative Adaptable Information Systems. The Next Generation of Information Systems 1991 : 169-184  DiSC'02 © 2003 Association for Computing Machinery |
