ML Anthology

Smooth Boosting for Margin-Based Ranking

Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, Masayuki Takeda
ALT 2008 pp. 227-239
doi:10.1007/978-3-540-87987-9_21 /alt/2008/moribe2008alt-smooth/

Abstract

We propose a new boosting algorithm for bipartite ranking problems. Our boosting algorithm, called SoftRankBoost, is a modification of RankBoost which maintains only smooth distributions over data. SoftRankBoost provably achieves approximately the maximum soft margin over all pairs of positive and negative examples, which implies high AUC score for future data.

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Text

Moribe et al. "Smooth Boosting for Margin-Based Ranking." International Conference on Algorithmic Learning Theory, 2008. doi:10.1007/978-3-540-87987-9_21

Markdown

[Moribe et al. "Smooth Boosting for Margin-Based Ranking." International Conference on Algorithmic Learning Theory, 2008.](https://mlanthology.org/alt/2008/moribe2008alt-smooth/) doi:10.1007/978-3-540-87987-9_21

BibTeX

@inproceedings{moribe2008alt-smooth,
  title     = {{Smooth Boosting for Margin-Based Ranking}},
  author    = {Moribe, Jun-ichi and Hatano, Kohei and Takimoto, Eiji and Takeda, Masayuki},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {2008},
  pages     = {227-239},
  doi       = {10.1007/978-3-540-87987-9_21},
  url       = {https://mlanthology.org/alt/2008/moribe2008alt-smooth/}
}