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Support Vector Regression Based on Unconstrained Convex Quadratic Programming

  • Weida Zhou
  • Li Zhang
  • Licheng Jiao
  • Jin Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

Support vector regression (SVR) based on unconstrained convex quadratic programming is proposed, in which Gaussian loss function is adopted. Compared with standard SVR, this method has a fast training speed and can be generalized into the complex-valued field directly. Experimental results confirm the feasibility and the validity of our method.

Keywords

Quadratic Programming Support Vector Regression Sinc Function Generalize Inverse Matrix Conjugate Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weida Zhou
    • 1
  • Li Zhang
    • 1
  • Licheng Jiao
    • 1
  • Jin Pan
    • 2
  1. 1.Institute of Intelligence Information ProcessingXidian UniversityChina
  2. 2.Xi’an Communications InstituteChina

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