Ranking with Large Margin Principle: Two Approaches

Amnon Shashua, Anat Levin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

96 Scopus citations

Abstract

We discuss the problem of ranking k instances with the use of a "large margin" principle. We introduce two main approaches: the first is the "fixed margin" policy in which the margin of the closest neighboring classes is being maximized - which turns out to be a direct generalization of SVM to ranking learning. The second approach allows for k - 1 different margins where the sum of margins is maximized. This approach is shown to reduce to lI-SVM when the number of classes k = 2. Both approaches are optimal in size of 21 where I is the total number of training examples. Experiments performed on visual classification and "collaborative filtering" show that both approaches outperform existing ordinal regression algorithms applied for ranking and multi-class SVM applied to general multi-class classification.

Original languageAmerican English
Title of host publicationNIPS 2002
Subtitle of host publicationProceedings of the 15th International Conference on Neural Information Processing Systems
EditorsSuzanna Becker, Sebastian Thrun, Klaus Obermayer
PublisherMIT Press Journals
Pages937-944
Number of pages8
ISBN (Electronic)0262025507, 9780262025508
StatePublished - 2002
Event15th International Conference on Neural Information Processing Systems, NIPS 2002 - Vancouver, Canada
Duration: 9 Dec 200214 Dec 2002

Publication series

NameNIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Conference

Conference15th International Conference on Neural Information Processing Systems, NIPS 2002
Country/TerritoryCanada
CityVancouver
Period9/12/0214/12/02

Bibliographical note

Publisher Copyright:
© NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.

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