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Maytal Saar-Tsechansky: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Kerem Tomak
    Adaptive mechanism design: a metalearning approach. [Citation Graph (0, 0)][DBLP]
    ICEC, 2006, pp:92-102 [Conf]
  2. Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney
    Active Learning for Probability Estimation Using Jensen-Shannon Divergence. [Citation Graph (0, 0)][DBLP]
    ECML, 2005, pp:268-279 [Conf]
  3. Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Mark Tsechansky
    Patterns Extraction for Monitoring Medical Practices. [Citation Graph (0, 0)][DBLP]
    HICSS, 2001, pp:- [Conf]
  4. 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]
  5. 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]
  6. 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]
  7. Bianca Zadrozny, Gary Weiss, Maytal Saar-Tsechansky
    UBDM 2006: Utility-Based Data Mining 2006 workshop report. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2006, v:8, n:2, pp:98-101 [Journal]
  8. Gary Weiss, Maytal Saar-Tsechansky, Bianca Zadrozny
    Report on UBDM-05: Workshop on Utility-Based Data Mining. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2005, v:7, n:2, pp:145-147 [Journal]
  9. 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]

  10. Guest editorial: special issue on utility-based data mining. [Citation Graph (, )][DBLP]

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