| |
 |      |
                                               |
Note: Links lead to the DBLP on the Web. Foster J. Provost Prem Melville , Foster J. Provost, Raymond J. Mooney : An Expected Utility Approach to Active Feature-Value Acquisition. ICDM 2005 : 745-748 Sofus A. Macskassy , Foster J. Provost, Saharon Rosset : ROC confidence bands: an empirical evaluation. ICML 2005 : 537-544 Abraham Bernstein , Foster J. Provost, Shawndra Hill : Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification. IEEE Trans. Knowl. Data Eng. 17 (4): 503-518 (2005) Venkateswarlu Kolluri , Foster J. Provost, Bruce G. Buchanan , Douglas Metzler : Knowledge Discovery Using Concept-Class Taxonomies. Australian Conference on Artificial Intelligence 2004 : 450-461 Prem Melville , Maytal Saar-Tsechansky , Foster J. Provost, Raymond J. Mooney : Active Feature-Value Acquisition for Classifier Induction. ICDM 2004 : 483-486 Sofus A. Macskassy , Foster J. Provost: Confidence Bands for ROC Curves: Methods and an Empirical Study. ROCAI 2004 : 61-70 Maytal Saar-Tsechansky , Foster J. Provost: Active Sampling for Class Probability Estimation and Ranking. Machine Learning 54 (2): 153-178 (2004) Claudia Perlich , Foster J. Provost: Aggregation-based feature invention and relational concept classes. KDD 2003 : 167-176 Gary M. Weiss , Foster J. Provost: Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. J. Artif. Intell. Res. (JAIR) 19 : 315-354 (2003) Claudia Perlich , Foster J. Provost, Jeffrey S. Simonoff : Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. Journal of Machine Learning Research 4 : 211-255 (2003) Foster J. Provost, Pedro Domingos : Tree Induction for Probability-Based Ranking. Machine Learning 52 (3): 199-215 (2003) Claudia Perlich , Foster J. Provost, Sofus A. Macskassy : Predicting citation rates for physics papers: constructing features for an ordered probit model. SIGKDD Explorations 5 (2): 154-155 (2003) Shawndra Hill , Foster J. Provost: The myth of the double-blind review?: author identification using only citations. SIGKDD Explorations 5 (2): 179-184 (2003) Maytal Saar-Tsechansky , Foster J. Provost: Active Learning for Class Probability Estimation and Ranking. IJCAI 2001 : 911-920 Sofus A. Macskassy , Haym Hirsh , Foster J. Provost, Ramesh Sankaranarayanan , Vasant Dhar : Intelligent Information Triage. SIGIR 2001 : 318-326 Ron Kohavi , Foster J. Provost: Applications of Data Mining to Electronic Commerce. Data Min. Knowl. Discov. 5 (1/2): 5-10 (2001) Foster J. Provost, Tom Fawcett : Robust Classification for Imprecise Environments. Machine Learning 42 (3): 203-231 (2001) Foster J. Provost, Tom Fawcett : Robust Classification for Imprecise Environments CoRR cs.LG/0009007 : (2000) Ron Kohavi , Foster J. Provost: Applications of Data Mining to Electronic Commerce CoRR cs.LG/0010006 : (2000) Vasant Dhar , Dashin Chou , Foster J. Provost: Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid with Entropy Reduction. Data Min. Knowl. Discov. 4 (4): 251-280 (2000) Foster J. Provost, David Jensen , Tim Oates : Efficient Progressive Sampling. KDD 1999 : 23-32 Tom Fawcett , Foster J. Provost: Activity Monitoring: Noticing Interesting Changes in Behavior. KDD 1999 : 53-62 Foster J. Provost, Venkateswarlu Kolluri : A Survey of Methods for Scaling Up Inductive Algorithms. Data Min. Knowl. Discov. 3 (2): 131-169 (1999) Foster J. Provost, Andrea Pohoreckyj Danyluk : Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. Informatica (Slovenia) 23 (1): (1999) Foster J. Provost, Tom Fawcett : Robust Classification Systems for Imprecise Environments. AAAI/IAAI 1998 : 706-713 Foster J. Provost, Tom Fawcett , Ron Kohavi : The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML 1998 : 445-453 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) Foster J. Provost, Ron Kohavi : Guest Editors' Introduction: On Applied Research in Machine Learning. Machine Learning 30 (2-3): 127-132 (1998) John M. Aronis , Foster J. Provost: Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. KDD 1997 : 119-122 Foster J. Provost, Venkateswarlu Kolluri : Scaling Up Inductive Algorithms: An Overview. KDD 1997 : 239-242 Foster J. Provost, Tom Fawcett : Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD 1997 : 43-48 Tom Fawcett , Foster J. Provost: Adaptive Fraud Detection. Data Min. Knowl. Discov. 1 (3): 291-316 (1997) Foster J. Provost, Daniel N. Hennessy : Scaling Up: Distributed Machine Learning with Cooperation. AAAI/IAAI, Vol. 1 1996 : 74-79 John M. Aronis , Foster J. Provost, Bruce G. Buchanan : Exploiting Background Knowledge in Automated Discovery. KDD 1996 : 355-358 Tom Fawcett , Foster J. Provost: Combining Data Mining and Machine Learning for Effective User Profiling. KDD 1996 : 8-13 Foster J. Provost, John M. Aronis : Scaling Up Inductive Learning with Massive Parallelism. Machine Learning 23 (1): 33-46 (1996) Foster J. Provost, Bruce G. Buchanan : Inductive Policy: The Pragmatics of Bias Selection. Machine Learning 20 (1-2): 35-61 (1995) Foster J. Provost, Daniel N. Hennessy : Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism. ISMB 1994 : 340-347 John M. Aronis , Foster J. Provost: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. KDD Workshop 1994 : 347-358 Foster J. Provost: Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. AAAI 1993 : 749-755 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 Foster J. Provost, Bruce G. Buchanan : Inductive Policy. AAAI 1992 : 255-261 Foster J. Provost, Bruce G. Buchanan : Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search. AII 1992 : 294-304 Foster J. Provost: ClimBS: Searching the Bias Space. ICTAI 1992 : 146-153 Foster J. Provost, Rami G. Melhem : A Distributed Algorithm for Embedding Trees in Hypercubes with Modifications for Run-Time Fault Tolerance. J. Parallel Distrib. Comput. 14 (1): 85-89 (1992) 1 [ 7 ] [ 10 ] [ 12 ] [ 17 ] 2 [ 43 ] 3 [ 3 ] [ 4 ] [ 9 ] [ 12 ] [ 42 ] 4 [ 26 ] 5 [ 5 ] [ 22 ] 6 [ 26 ] [ 31 ] 7 [ 35 ] 8 [ 11 ] [ 14 ] [ 15 ] [ 19 ] [ 20 ] [ 21 ] [ 24 ] [ 28 ] [ 29 ] 9 [ 19 ] 10 [ 8 ] [ 13 ] 11 [ 33 ] [ 43 ] 12 [ 31 ] 13 [ 25 ] 14 [ 18 ] [ 20 ] [ 27 ] [ 30 ] 15 [ 16 ] [ 23 ] [ 42 ] 16 [ 31 ] [ 34 ] [ 40 ] [ 44 ] 17 [ 1 ] 18 [ 41 ] [ 45 ] 19 [ 42 ] 20 [ 41 ] [ 45 ] 21 [ 25 ] 22 [ 34 ] [ 36 ] [ 38 ] 23 [ 44 ] 24 [ 32 ] [ 39 ] [ 41 ] 25 [ 31 ] 26 [ 36 ] 27 [ 19 ] 28 [ 37 ]  ©2006 Association for Computing Machinery |
