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Foster J. Provost: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. John M. Aronis, Foster J. Provost
    Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. [Citation Graph (1, 0)][DBLP]
    KDD Workshop, 1994, pp:347-358 [Conf]
  2. John M. Aronis, Foster J. Provost
    Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. [Citation Graph (1, 0)][DBLP]
    KDD, 1997, pp:119-122 [Conf]
  3. Foster J. Provost, Venkateswarlu Kolluri
    Scaling Up Inductive Algorithms: An Overview. [Citation Graph (1, 0)][DBLP]
    KDD, 1997, pp:239-242 [Conf]
  4. Tom Fawcett, Foster J. Provost
    Adaptive Fraud Detection. [Citation Graph (1, 0)][DBLP]
    Data Min. Knowl. Discov., 1997, v:1, n:3, pp:291-316 [Journal]
  5. Foster J. Provost
    Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias. [Citation Graph (0, 0)][DBLP]
    AAAI, 1993, pp:749-755 [Conf]
  6. Foster J. Provost, Bruce G. Buchanan
    Inductive Policy. [Citation Graph (0, 0)][DBLP]
    AAAI, 1992, pp:255-261 [Conf]
  7. Foster J. Provost, Tom Fawcett
    Robust Classification Systems for Imprecise Environments. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:706-713 [Conf]
  8. Foster J. Provost, Daniel N. Hennessy
    Scaling Up: Distributed Machine Learning with Cooperation. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, Vol. 1, 1996, pp:74-79 [Conf]
  9. Foster J. Provost, Bruce G. Buchanan
    Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search. [Citation Graph (0, 0)][DBLP]
    AII, 1992, pp:294-304 [Conf]
  10. Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan, Douglas Metzler
    Knowledge Discovery Using Concept-Class Taxonomies. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:450-461 [Conf]
  11. Prem Melville, Foster J. Provost, Raymond J. Mooney
    An Expected Utility Approach to Active Feature-Value Acquisition. [Citation Graph (0, 0)][DBLP]
    ICDM, 2005, pp:745-748 [Conf]
  12. Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney
    Active Feature-Value Acquisition for Classifier Induction. [Citation Graph (0, 0)][DBLP]
    ICDM, 2004, pp:483-486 [Conf]
  13. Andrea Pohoreckyj Danyluk, Foster J. Provost
    Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network. [Citation Graph (0, 0)][DBLP]
    ICML, 1993, pp:81-88 [Conf]
  14. Sofus A. Macskassy, Foster J. Provost, Saharon Rosset
    ROC confidence bands: an empirical evaluation. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:537-544 [Conf]
  15. Foster J. Provost, Tom Fawcett, Ron Kohavi
    The Case against Accuracy Estimation for Comparing Induction Algorithms. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:445-453 [Conf]
  16. Foster J. Provost
    ClimBS: Searching the Bias Space. [Citation Graph (0, 0)][DBLP]
    ICTAI, 1992, pp:146-153 [Conf]
  17. Maytal Saar-Tsechansky, Foster J. Provost
    Active Learning for Class Probability Estimation and Ranking. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2001, pp:911-920 [Conf]
  18. Foster J. Provost, Daniel N. Hennessy
    Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism. [Citation Graph (0, 0)][DBLP]
    ISMB, 1994, pp:340-347 [Conf]
  19. John M. Aronis, Foster J. Provost, Bruce G. Buchanan
    Exploiting Background Knowledge in Automated Discovery. [Citation Graph (0, 0)][DBLP]
    KDD, 1996, pp:355-358 [Conf]
  20. Tom Fawcett, Foster J. Provost
    Combining Data Mining and Machine Learning for Effective User Profiling. [Citation Graph (0, 0)][DBLP]
    KDD, 1996, pp:8-13 [Conf]
  21. Tom Fawcett, Foster J. Provost
    Activity Monitoring: Noticing Interesting Changes in Behavior. [Citation Graph (0, 0)][DBLP]
    KDD, 1999, pp:53-62 [Conf]
  22. Claudia Perlich, Foster J. Provost
    Aggregation-based feature invention and relational concept classes. [Citation Graph (0, 0)][DBLP]
    KDD, 2003, pp:167-176 [Conf]
  23. Foster J. Provost, Tom Fawcett
    Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:43-48 [Conf]
  24. Foster J. Provost, David Jensen, Tim Oates
    Efficient Progressive Sampling. [Citation Graph (0, 0)][DBLP]
    KDD, 1999, pp:23-32 [Conf]
  25. Sofus A. Macskassy, Foster J. Provost
    Confidence Bands for ROC Curves: Methods and an Empirical Study. [Citation Graph (0, 0)][DBLP]
    ROCAI, 2004, pp:61-70 [Conf]
  26. Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar
    Intelligent Information Triage. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2001, pp:318-326 [Conf]
  27. Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo
    AI Approaches to Fraud Detection and Risk Management. [Citation Graph (0, 0)][DBLP]
    AI Magazine, 1998, v:19, n:2, pp:107-108 [Journal]
  28. Foster J. Provost, Tom Fawcett
    Robust Classification for Imprecise Environments [Citation Graph (0, 0)][DBLP]
    CoRR, 2000, v:0, n:, pp:- [Journal]
  29. Ron Kohavi, Foster J. Provost
    Applications of Data Mining to Electronic Commerce [Citation Graph (0, 0)][DBLP]
    CoRR, 2000, v:0, n:, pp:- [Journal]
  30. Ron Kohavi, Foster J. Provost
    Applications of Data Mining to Electronic Commerce. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2001, v:5, n:1/2, pp:5-10 [Journal]
  31. Vasant Dhar, Dashin Chou, Foster J. Provost
    Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid with Entropy Reduction. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2000, v:4, n:4, pp:251-280 [Journal]
  32. Foster J. Provost, Venkateswarlu Kolluri
    A Survey of Methods for Scaling Up Inductive Algorithms. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 1999, v:3, n:2, pp:131-169 [Journal]
  33. Foster J. Provost, Andrea Pohoreckyj Danyluk
    Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study. [Citation Graph (0, 0)][DBLP]
    Informatica (Slovenia), 1999, v:23, n:1, pp:- [Journal]
  34. Gary M. Weiss, Foster J. Provost
    Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2003, v:19, n:, pp:315-354 [Journal]
  35. Claudia Perlich, Foster J. Provost, Jeffrey S. Simonoff
    Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2003, v:4, n:, pp:211-255 [Journal]
  36. Foster J. Provost, Rami G. Melhem
    A Distributed Algorithm for Embedding Trees in Hypercubes with Modifications for Run-Time Fault Tolerance. [Citation Graph (0, 0)][DBLP]
    J. Parallel Distrib. Comput., 1992, v:14, n:1, pp:85-89 [Journal]
  37. Claudia Perlich, Foster J. Provost
    Distribution-based aggregation for relational learning with identifier attributes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2006, v:62, n:1-2, pp:65-105 [Journal]
  38. Foster J. Provost, John M. Aronis
    Scaling Up Inductive Learning with Massive Parallelism. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:23, n:1, pp:33-46 [Journal]
  39. Foster J. Provost, Bruce G. Buchanan
    Inductive Policy: The Pragmatics of Bias Selection. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1995, v:20, n:1-2, pp:35-61 [Journal]
  40. Foster J. Provost, Pedro Domingos
    Tree Induction for Probability-Based Ranking. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:52, n:3, pp:199-215 [Journal]
  41. Foster J. Provost, Tom Fawcett
    Robust Classification for Imprecise Environments. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:42, n:3, pp:203-231 [Journal]
  42. Foster J. Provost, Ron Kohavi
    Guest Editors' Introduction: On Applied Research in Machine Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1998, v:30, n:2-3, pp:127-132 [Journal]
  43. Maytal Saar-Tsechansky, Foster J. Provost
    Active Sampling for Class Probability Estimation and Ranking. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:54, n:2, pp:153-178 [Journal]
  44. Shawndra Hill, Foster J. Provost
    The myth of the double-blind review?: author identification using only citations. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:2, pp:179-184 [Journal]
  45. Claudia Perlich, Foster J. Provost, Sofus A. Macskassy
    Predicting citation rates for physics papers: constructing features for an ordered probit model. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:2, pp:154-155 [Journal]
  46. Abraham Bernstein, Foster J. Provost, Shawndra Hill
    Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Knowl. Data Eng., 2005, v:17, n:4, pp:503-518 [Journal]
  47. Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky
    Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. [Citation Graph (0, 0)][DBLP]
    ICEC, 2007, pp:389-398 [Conf]
  48. Foster J. Provost, Arun Sundararajan
    Modeling complex networks for electronic commerce. [Citation Graph (0, 0)][DBLP]
    ACM Conference on Electronic Commerce, 2007, pp:368- [Conf]

  49. Get another label? improving data quality and data mining using multiple, noisy labelers. [Citation Graph (, )][DBLP]


  50. Audience selection for on-line brand advertising: privacy-friendly social network targeting. [Citation Graph (, )][DBLP]


  51. Brand advertising, on-line audiences, and social media: invited talk. [Citation Graph (, )][DBLP]


  52. Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance. [Citation Graph (, )][DBLP]


  53. A Unified Approach to Active Dual Supervision for Labeling Features and Examples. [Citation Graph (, )][DBLP]


  54. Learning and Inference in Massive Social Networks. [Citation Graph (, )][DBLP]


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