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Support Vector Regression Based on Unconstrained Convex Quadratic Programming

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

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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.

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References

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Author information

Authors and Affiliations

  1. Institute of Intelligence Information Processing, Xidian University, Xi’an, 710071, China

    Weida Zhou, Li Zhang & Licheng Jiao

  2. Xi’an Communications Institute, Xi’an, 710106, China

    Jin Pan

Authors
  1. Weida Zhou
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  2. Li Zhang
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  3. Licheng Jiao
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  4. Jin Pan
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., 710071, Xi’an, P.R. China

    Xin-bo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhou, W., Zhang, L., Jiao, L., Pan, J. (2006). Support Vector Regression Based on Unconstrained Convex Quadratic Programming. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_27

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  • DOI: https://doi.org/10.1007/11881070_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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