ML Anthology

Linear Programming Boosting for Uneven Datasets

Jure Leskovec, John Shawe-Taylor
ICML 2003 pp. 456-463
/icml/2003/leskovec2003icml-linear/

Abstract

The paper extends the notion of linear programming boosting to handle uneven datasets. Extensive experiments with text classification problem compare the performance of a number of different boosting strategies, concentrating on the problems posed by uneven datasets. ICML Proceedings of the Twentieth International Conference on Machine Learning

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Cite

Text

Leskovec and Shawe-Taylor. "Linear Programming Boosting for Uneven Datasets." International Conference on Machine Learning, 2003.

Markdown

[Leskovec and Shawe-Taylor. "Linear Programming Boosting for Uneven Datasets." International Conference on Machine Learning, 2003.](https://mlanthology.org/icml/2003/leskovec2003icml-linear/)

BibTeX

@inproceedings{leskovec2003icml-linear,
  title     = {{Linear Programming Boosting for Uneven Datasets}},
  author    = {Leskovec, Jure and Shawe-Taylor, John},
  booktitle = {International Conference on Machine Learning},
  year      = {2003},
  pages     = {456-463},
  url       = {https://mlanthology.org/icml/2003/leskovec2003icml-linear/}
}