Foster J. Provost

2007
49	EE	Foster J. Provost, Arun Sundararajan: Modeling complex networks for electronic commerce. ACM Conference on Electronic Commerce 2007: 368
48	EE	Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky: Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC 2007: 389-398
47	EE	Shawndra Hill, Foster J. Provost, Chris Volinsky: Learning and Inference in Massive Social Networks. MLG 2007
2006
46	EE	Claudia Perlich, Foster J. Provost: Distribution-based aggregation for relational learning with identifier attributes. Machine Learning 62(1-2): 65-105 (2006)
2005
45	EE	Prem Melville, Foster J. Provost, Raymond J. Mooney: An Expected Utility Approach to Active Feature-Value Acquisition. ICDM 2005: 745-748
44	EE	Sofus A. Macskassy, Foster J. Provost, Saharon Rosset: ROC confidence bands: an empirical evaluation. ICML 2005: 537-544
43	EE	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)
2004
42	EE	Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler: Knowledge Discovery Using Concept-Class Taxonomies. Australian Conference on Artificial Intelligence 2004: 450-461
41	EE	Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney: Active Feature-Value Acquisition for Classifier Induction. ICDM 2004: 483-486
40		Sofus A. Macskassy, Foster J. Provost: Confidence Bands for ROC Curves: Methods and an Empirical Study. ROCAI 2004: 61-70
39	EE	Maytal Saar-Tsechansky, Foster J. Provost: Active Sampling for Class Probability Estimation and Ranking. Machine Learning 54(2): 153-178 (2004)
2003
38	EE	Claudia Perlich, Foster J. Provost: Aggregation-based feature invention and relational concept classes. KDD 2003: 167-176
37	EE	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)
36	EE	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)
35	EE	Foster J. Provost, Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3): 199-215 (2003)
34	EE	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)
33	EE	Shawndra Hill, Foster J. Provost: The myth of the double-blind review?: author identification using only citations. SIGKDD Explorations 5(2): 179-184 (2003)
2001
32		Maytal Saar-Tsechansky, Foster J. Provost: Active Learning for Class Probability Estimation and Ranking. IJCAI 2001: 911-920
31		Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar: Intelligent Information Triage. SIGIR 2001: 318-326
30		Ron Kohavi, Foster J. Provost: Applications of Data Mining to Electronic Commerce. Data Min. Knowl. Discov. 5(1/2): 5-10 (2001)
29		Foster J. Provost, Tom Fawcett: Robust Classification for Imprecise Environments. Machine Learning 42(3): 203-231 (2001)
2000
28	EE	Foster J. Provost, Tom Fawcett: Robust Classification for Imprecise Environments CoRR cs.LG/0009007: (2000)
27	EE	Ron Kohavi, Foster J. Provost: Applications of Data Mining to Electronic Commerce CoRR cs.LG/0010006: (2000)
26		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)
1999
25	EE	Foster J. Provost, David Jensen, Tim Oates: Efficient Progressive Sampling. KDD 1999: 23-32
24	EE	Tom Fawcett, Foster J. Provost: Activity Monitoring: Noticing Interesting Changes in Behavior. KDD 1999: 53-62
23		Foster J. Provost, Venkateswarlu Kolluri: A Survey of Methods for Scaling Up Inductive Algorithms. Data Min. Knowl. Discov. 3(2): 131-169 (1999)
22		Foster J. Provost, Andrea Pohoreckyj Danyluk: Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. Informatica (Slovenia) 23(1): (1999)
1998
21		Foster J. Provost, Tom Fawcett: Robust Classification Systems for Imprecise Environments. AAAI/IAAI 1998: 706-713
20		Foster J. Provost, Tom Fawcett, Ron Kohavi: The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML 1998: 445-453
19		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)
18		Foster J. Provost, Ron Kohavi: Guest Editors' Introduction: On Applied Research in Machine Learning. Machine Learning 30(2-3): 127-132 (1998)
1997
17		John M. Aronis, Foster J. Provost: Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. KDD 1997: 119-122
16		Foster J. Provost, Venkateswarlu Kolluri: Scaling Up Inductive Algorithms: An Overview. KDD 1997: 239-242
15		Foster J. Provost, Tom Fawcett: Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD 1997: 43-48
14		Tom Fawcett, Foster J. Provost: Adaptive Fraud Detection. Data Min. Knowl. Discov. 1(3): 291-316 (1997)
1996
13		Foster J. Provost, Daniel N. Hennessy: Scaling Up: Distributed Machine Learning with Cooperation. AAAI/IAAI, Vol. 1 1996: 74-79
12		John M. Aronis, Foster J. Provost, Bruce G. Buchanan: Exploiting Background Knowledge in Automated Discovery. KDD 1996: 355-358
11		Tom Fawcett, Foster J. Provost: Combining Data Mining and Machine Learning for Effective User Profiling. KDD 1996: 8-13
10		Foster J. Provost, John M. Aronis: Scaling Up Inductive Learning with Massive Parallelism. Machine Learning 23(1): 33-46 (1996)
1995
9		Foster J. Provost, Bruce G. Buchanan: Inductive Policy: The Pragmatics of Bias Selection. Machine Learning 20(1-2): 35-61 (1995)
1994
8		Foster J. Provost, Daniel N. Hennessy: Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism. ISMB 1994: 340-347
7		John M. Aronis, Foster J. Provost: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. KDD Workshop 1994: 347-358
1993
6		Foster J. Provost: Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. AAAI 1993: 749-755
5		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
1992
4		Foster J. Provost, Bruce G. Buchanan: Inductive Policy. AAAI 1992: 255-261
3		Foster J. Provost, Bruce G. Buchanan: Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search. AII 1992: 294-304
2		Foster J. Provost: ClimBS: Searching the Bias Space. ICTAI 1992: 146-153
1		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)

Coauthor Index

1	John M. Aronis	[7] [10] [12] [17]
2	Abraham Bernstein	[43]
3	Bruce G. Buchanan	[3] [4] [9] [12] [42]
4	Dashin Chou	[26]
5	Andrea Pohoreckyj Danyluk	[5] [22]
6	Vasant Dhar	[26] [31]
7	Pedro Domingos	[35]
8	Tom Fawcett	[11] [14] [15] [19] [20] [21] [24] [28] [29]
9	Ira J. Haimowitz	[19]
10	Daniel N. Hennessy	[8] [13]
11	Shawndra Hill	[33] [43] [47]
12	Haym Hirsh	[31]
13	David Jensen	[25]
14	Ron Kohavi	[18] [20] [27] [30]
15	Venkateswarlu Kolluri	[16] [23] [42]
16	Sofus A. Macskassy	[31] [34] [40] [44]
17	Rami G. Melhem	[1]
18	Prem Melville	[41] [45] [48]
19	Douglas Metzler	[42]
20	Raymond J. Mooney	[41] [45]
21	Tim Oates	[25]
22	Claudia Perlich	[34] [36] [38] [46]
23	Saharon Rosset	[44]
24	Maytal Saar-Tsechansky	[32] [39] [41] [48]
25	Ramesh Sankaranarayanan	[31]
26	Jeffrey S. Simonoff	[36]
27	Salvatore J. Stolfo	[19]
28	Arun Sundararajan	[49]
29	Chris Volinsky	[47]
30	Gary M. Weiss	[37]

Colors in the list of coauthors