Building Support Vector Machine Alternative Using Algorithms of Computational Geometry

  • Marek Bundzel
  • Tomáš Kasanický
  • Baltazár Frankovič
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


The task of pattern recognition is a task of division of a feature space into regions separating the training examples belonging to different classes. Support Vector Machines (SVM) identify the most borderline examples called support vectors and use them to determine discrimination hyperplanes (hyper–curves). In this paper a pattern recognition method is proposed which represents an alternative to SVM algorithm. Support vectors are identified using selected methods of computational geometry in the original space of features i.e. not in the transformed space determined partially by the kernel function of SVM. The proposed algorithm enables usage of kernel functions. The separation task is reduced to a search for an optimal separating hyperplane or a Winner Takes All (WTA) principle is applied.


Support Vector Machine Support Vector Kernel Function Computational Geometry Delaunay Triangulation 
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

  • Marek Bundzel
    • 1
  • Tomáš Kasanický
    • 1
    • 2
  • Baltazár Frankovič
    • 2
  1. 1.Faculty of Electrical Engineering and Informatics, Department of Cybernetics and Artificial IntelligenceTechnical University of KošiceKošiceSlovak Republic
  2. 2.Institute of InformaticsSlovak Academy of SciencesBratislava 45Slovak Republic

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