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Dimension Reduction for Object Ranking

  • Toshihiro Kamishima
  • Shotaro Akaho
Chapter

Abstract

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and bestseller lists. Techniques for processing such ordinal data are being developed, particularly methods for an object ranking task: i.e., learning functions used to sort objects from sample orders. In this article, we propose two dimension reduction methods specifically designed to improve prediction performance in an object ranking task.

Notes

Acknowledgements

This work is supported by the grants-in-aid 14658106 and 16700157 of the Japan society for the promotion of science. Thanks are due to the Mainichi Newspapers for permission to use the articles. We would also like to thank Thorsten Joachims for providing the { SVM} light software.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan

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