Building support vector machine alternative using algorithms of computational geometry

  • Authors:

  • Marek Bundzel;Tomáš Kasanický;Baltazár Frankovič

  • Affiliations:

  • Faculty of Electrical Engineering and Informatics, Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Košice, Slovak Republic;Faculty of Electrical Engineering and Informatics, Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Košice, Slovak Republic;Institute of Informatics, Slovak Academy of Sciences, Bratislava 45, Slovak Republic

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

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.