A New Algorithm for Computing the Minimal Enclosing Sphere in Feature Space

  • Chonghui Guo
  • Mingyu Lu
  • Jiantao Sun
  • Yuchang Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)


The problem of computing the minimal enclosing sphere (MES) of a set of points in the high dimensional kernel-induced feature space is considered. In this paper we develop an entropy-based algorithm that is suitable for any Mercer kernel mapping. The proposed algorithm is based on maximum entropy principle and it is very simple to implement. The convergence of the novel algorithm is analyzed and the validity of this algorithm is confirmed by preliminary numerical results.


Support Vector Feature Space Quadratic Programming Quadratic Programming Problem Maximum Entropy Principle 
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 2005

Authors and Affiliations

  • Chonghui Guo
    • 1
  • Mingyu Lu
    • 2
  • Jiantao Sun
    • 2
  • Yuchang Lu
    • 2
  1. 1.Department of Applied MathematicsDalian University of TechnologyDalianChina
  2. 2.Department of Computer ScienceTsinghua UniversityBeijingChina

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