Smooth boosting for margin-based ranking

Jun Ichi Moribe, Kohei Hatano, Eiji Takimoto, Masayuki Takeda

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)227-239
Number of pages13
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5254 LNAI
Publication statusPublished - 2008
Event19th International Conference on Algorithmic Learning Theory, ALT 2008 - Budapest, Hungary
Duration: Oct 13 2008Oct 16 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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