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Margin-based ranking meets boosting in the middle

Publication ,  Conference
Rudin, C; Cortes, C; Mohri, M; Schapire, RE
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2005

We present several results related to ranking. We give a general margin-based bound for ranking based on the L∞ covering number of the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin generalize well. We then describe a new algorithm, Smooth Margin Ranking, that precisely converges to a maximum ranking-margin solution. The algorithm is a modification of RankBoost, analogous to Approximate Coordinate Ascent Boosting. We also prove a remarkable property of AdaBoost: under very natural conditions, AdaBoost maximizes the exponentiated loss associated with the AUC and achieves the same AUC as RankBoost. This explains the empirical observations made by Cortes and Mohri, and Caruana and Niculescu-Mizil, about the excellent performance of AdaBoost as a ranking algorithm, as measured by the AUC. © Springer-Verlag Berlin Heidelberg 2005.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540265566

Publication Date

January 1, 2005

Volume

3559 LNAI

Start / End Page

63 / 78

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Rudin, C., Cortes, C., Mohri, M., & Schapire, R. E. (2005). Margin-based ranking meets boosting in the middle. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3559 LNAI, pp. 63–78). https://doi.org/10.1007/11503415_5
Rudin, C., C. Cortes, M. Mohri, and R. E. Schapire. “Margin-based ranking meets boosting in the middle.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3559 LNAI:63–78, 2005. https://doi.org/10.1007/11503415_5.
Rudin C, Cortes C, Mohri M, Schapire RE. Margin-based ranking meets boosting in the middle. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 63–78.
Rudin, C., et al. “Margin-based ranking meets boosting in the middle.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3559 LNAI, 2005, pp. 63–78. Scopus, doi:10.1007/11503415_5.
Rudin C, Cortes C, Mohri M, Schapire RE. Margin-based ranking meets boosting in the middle. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 63–78.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540265566

Publication Date

January 1, 2005

Volume

3559 LNAI

Start / End Page

63 / 78

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences