An Improved Training Algorithm for the Linear Ranking Support Vector Machine

  • Antti Airola
  • Tapio Pahikkala
  • Tapio Salakoski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6791)

Abstract

We introduce an O(ms + mlog(m)) time complexity method for training the linear ranking support vector machine, where m is the number of training examples, and s the average number of non-zero features per example. The method generalizes the fastest previously known approach, which achieves the same efficiency only in restricted special cases. The excellent scalability of the proposed method is demonstrated experimentally.

Keywords

binary search tree cutting plane optimization learning to rank support vector machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antti Airola
    • 1
  • Tapio Pahikkala
    • 1
  • Tapio Salakoski
    • 1
  1. 1.University of Turku and Turku Centre for Computer Science (TUCS)TurkuFinland

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