An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy

  • Ruxandra Stoean
  • Mike Preuss
  • Catalin Stoean
  • Elia El-Darzi
  • D. Dumitrescu
Part of the Studies in Computational Intelligence book series (SCI, volume 201)


Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled by a number of other approaches in view of the development of a superior hybridized technique. It is also the proposal of present chapter to bring support vector machines together with evolutionary computation, with the aim to offer a simplified solving version for the central optimization problem of determining the equation of the hyperplane deriving from support vector learning. The evolutionary approach suggested in this chapter resolves the complexity of the optimizer, opens the ‘blackbox’ of support vector training and breaks the limits of the canonical solving component.


Support Vector Machine Evolutionary Algorithm Support Vector Regression Decision Function Latin Hypercube Sampling 
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 2009

Authors and Affiliations

  • Ruxandra Stoean
    • 1
  • Mike Preuss
    • 2
  • Catalin Stoean
    • 1
  • Elia El-Darzi
    • 3
  • D. Dumitrescu
    • 4
  1. 1.Department of Computer ScienceUniversity of CraiovaRomania
  2. 2.Department of Computer ScienceUniversity of DortmundGermany
  3. 3.Department of Computer ScienceUniversity of WestminsterUK
  4. 4.Department of Computer ScienceUniversity of Cluj-NapocaRomania

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