Foster J. Provost Vis

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54 Paul N. Bennett, Raman Chandrasekar, Max Chickering, Panagiotis G. Ipeirotis, Edith Law, Anton Mityagin, Foster J. Provost, Luis von Ahn: Proceedings of the ACM SIGKDD Workshop on Human Computation, Paris, France, June 28, 2009 ACM 2009
53EEFoster J. Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, Alan Murray: Audience selection for on-line brand advertising: privacy-friendly social network targeting. KDD 2009: 707-716
52EEFoster J. Provost: Brand advertising, on-line audiences, and social media: invited talk. KDD Workshop on Data Mining and Audience Intelligence for Advertising 2009
51EEVictor S. Sheng, Foster J. Provost, Panagiotis G. Ipeirotis: Get another label? improving data quality and data mining using multiple, noisy labelers. KDD 2008: 614-622
50EEFoster J. Provost, Arun Sundararajan: Modeling complex networks for electronic commerce. ACM Conference on Electronic Commerce 2007: 368
49EEFoster J. Provost, Prem Melville, Maytal Saar-Tsechansky: Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC 2007: 389-398
48EEShawndra Hill, Foster J. Provost, Chris Volinsky: Learning and Inference in Massive Social Networks. MLG 2007
47EEMaytal Saar-Tsechansky, Foster J. Provost: Decision-Centric Active Learning of Binary-Outcome Models. Information Systems Research 18(1): 4-22 (2007)
46EEClaudia Perlich, Foster J. Provost: Distribution-based aggregation for relational learning with identifier attributes. Machine Learning 62(1-2): 65-105 (2006)
45EEPrem Melville, Foster J. Provost, Raymond J. Mooney: An Expected Utility Approach to Active Feature-Value Acquisition. ICDM 2005: 745-748
44EESofus A. Macskassy, Foster J. Provost, Saharon Rosset: ROC confidence bands: an empirical evaluation. ICML 2005: 537-544
43EEAbraham 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)
42EEVenkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler: Knowledge Discovery Using Concept-Class Taxonomies. Australian Conference on Artificial Intelligence 2004: 450-461
41EEPrem 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
39EEMaytal Saar-Tsechansky, Foster J. Provost: Active Sampling for Class Probability Estimation and Ranking. Machine Learning 54(2): 153-178 (2004)
38EEClaudia Perlich, Foster J. Provost: Aggregation-based feature invention and relational concept classes. KDD 2003: 167-176
37EEGary 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)
36EEClaudia 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)
35EEFoster J. Provost, Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3): 199-215 (2003)
34EEClaudia 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)
33EEShawndra Hill, Foster J. Provost: The myth of the double-blind review?: author identification using only citations. SIGKDD Explorations 5(2): 179-184 (2003)
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)
28EEFoster J. Provost, Tom Fawcett: Robust Classification for Imprecise Environments CoRR cs.LG/0009007: (2000)
27EERon 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)
25EEFoster J. Provost, David Jensen, Tim Oates: Efficient Progressive Sampling. KDD 1999: 23-32
24EETom 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)
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)
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)
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)
9 Foster J. Provost, Bruce G. Buchanan: Inductive Policy: The Pragmatics of Bias Selection. Machine Learning 20(1-2): 35-61 (1995)
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
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
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

1Luis von Ahn [54]
2John M. Aronis [7] [10] [12] [17]
3Paul N. Bennett [54]
4Abraham Bernstein [43]
5Bruce G. Buchanan [3] [4] [9] [12] [42]
6Raman Chandrasekar [54]
7David Maxwell Chickering (Max Chickering) [54]
8Dashin Chou [26]
9Brian Dalessandro [53]
10Andrea Pohoreckyj Danyluk [5] [22]
11Vasant Dhar [26] [31]
12Pedro Domingos [35]
13Tom Fawcett [11] [14] [15] [19] [20] [21] [24] [28] [29]
14Ira J. Haimowitz [19]
15Daniel N. Hennessy [8] [13]
16Shawndra Hill [33] [43] [48]
17Haym Hirsh [31]
18Rod Hook [53]
19Panagiotis G. Ipeirotis [51] [54]
20David Jensen [25]
21Ron Kohavi [18] [20] [27] [30]
22Venkateswarlu Kolluri [16] [23] [42]
23Edith Law [54]
24Sofus A. Macskassy [31] [34] [40] [44]
25Rami G. Melhem [1]
26Prem Melville [41] [45] [49]
27Douglas Metzler [42]
28Anton Mityagin [54]
29Raymond J. Mooney [41] [45]
30Alan Murray [53]
31Tim Oates [25]
32Claudia Perlich [34] [36] [38] [46]
33Saharon Rosset [44]
34Maytal Saar-Tsechansky [32] [39] [41] [47] [49]
35Ramesh Sankaranarayanan [31]
36Victor S. Sheng [51]
37Jeffrey S. Simonoff [36]
38Salvatore J. Stolfo [19]
39Arun Sundararajan [50]
40Chris Volinsky [48]
41Gary M. Weiss [37]
42Xiaohan Zhang [53]

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Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)