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Probabilistic Ranking Support Vector Machine

  • Nguyen Thi Thanh Thuy
  • Ngo Anh Vien
  • Nguyen Hoang Viet
  • TaeChoong Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

Recently, Support Vector Machines (SVMs) have been applied very effectively in learning ranking functions (or preference functions).They intend to learn ranking functions with the principles of the large margin and the kernel trick. However, the output of a ranking function is a score function which is not a calibrated posterior probability to enable post-processing. One approach to deal with this problem is to apply a generalized linear model with a link function and solve it by calculating the maximum likelihood estimate. But, if the link function is nonlinear, maximizing the likelihood will face with difficulties. Instead, we propose a new approach which train an SVM for a ranking function, then map the SVM outputs into a probabilistic sigmoid function whose parameters are trained by using cross-validation. This method will be tested on three data-mining datasets and compared to the results obtained by standard SVMs.

Keywords

SVM Ranking SVM Probabilistic SVM Probabilistic ranking SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nguyen Thi Thanh Thuy
    • 1
  • Ngo Anh Vien
    • 1
  • Nguyen Hoang Viet
    • 1
  • TaeChoong Chung
    • 1
  1. 1.Artificial Intelligence Lab, Department of Computer Engineering, School of Electronics and InformationKyungHee University SeoCheon, GiHeung, YongIn, GyeongGiDoSouth Korea

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