Support vector regression based on unconstrained convex quadratic programming

  • Authors:

  • Weida Zhou;Li Zhang;Licheng Jiao;Jin Pan

  • Affiliations:

  • Institute of Intelligence Information Processing, Xidian University, China;Institute of Intelligence Information Processing, Xidian University, China;Institute of Intelligence Information Processing, Xidian University, China;Xi'an Communications Institute, China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2006

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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.