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MINLIP: Efficient Learning of Transformation Models

  • Vanya Van Belle
  • Kristiaan Pelckmans
  • Johan A. K. Suykens
  • Sabine Van Huffel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidate to study ranking models and ordinal regression in a context of machine learning. We do implement a structural risk minimization strategy based on a Lipschitz smoothness condition of the transformation model. Then, it is shown how the estimate can be obtained efficiently by solving a convex quadratic program with O(n) linear constraints and unknowns, with n the number of data points. A set of experiments do support these findings.

Keywords

Support vector machines ranking models ordinal regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vanya Van Belle
    • 1
  • Kristiaan Pelckmans
    • 1
  • Johan A. K. Suykens
    • 1
  • Sabine Van Huffel
    • 1
  1. 1.Katholieke Universiteit Leuven, ESAT-SCDLeuvenBelgium

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