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Gary M. Weiss: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Gary M. Weiss, Johannes P. Ros, Anoop Singhal
    ANSWER: Network Monitoring Using Object-Oriented Rules. [Citation Graph (1, 0)][DBLP]
    AAAI/IAAI, 1998, pp:1087-1093 [Conf]
  2. Gary M. Weiss, Haym Hirsh
    A Quantitative Study of Small Disjuncts. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2000, pp:665-670 [Conf]
  3. Gary M. Weiss
    Learning with Rare Cases and Small Disjuncts. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:558-565 [Conf]
  4. Gary M. Weiss, Haym Hirsh
    The Problem with Noise and Small Disjuncts. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:574-0 [Conf]
  5. Gary M. Weiss, Haym Hirsh
    Learning to Predict Rare Events in Event Sequences. [Citation Graph (0, 0)][DBLP]
    KDD, 1998, pp:359-363 [Conf]
  6. 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]
  7. Gary M. Weiss, Johannes P. Ros
    Implementing Design Patterns with Object-Oriented Rules. [Citation Graph (0, 0)][DBLP]
    JOOP, 1998, v:11, n:7, pp:25-27 [Journal]
  8. Gary M. Weiss, Ye Tian
    Maximizing classifier utility when training data is costly. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2006, v:8, n:2, pp:31-38 [Journal]
  9. Gary M. Weiss
    Mining with rarity: a unifying framework. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:1, pp:7-19 [Journal]

  10. Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs? [Citation Graph (, )][DBLP]

  11. Are Decision Trees Always Greener on the Open (Source) Side of the Fence? [Citation Graph (, )][DBLP]

  12. Data Mining in the Real World: Experiences, Challenges, and Recommendations. [Citation Graph (, )][DBLP]

  13. A Combinatorial Fusion Method for Feature Construction. [Citation Graph (, )][DBLP]

  14. Quantification and semi-supervised classification methods for handling changes in class distribution. [Citation Graph (, )][DBLP]

  15. Maximizing classifier utility when there are data acquisition and modeling costs. [Citation Graph (, )][DBLP]

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

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