A Geometrical Approach for Parameter Selection of Radial Basis Functions Networks

  • Luiz C. B. Torres
  • André P. Lemos
  • Cristiano L. Castro
  • Antônio P. Braga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


The RBF network is commonly used for classification and function approximation. The center and radius of the activation function of neurons is an important parameter to be found before the network training. This paper presents a method based on computational geometry to find these coefficients without any parameters provided by the user. The method is compared with a SVM and experimental results showed that our approach is promising.


machine learning classification RBF neural network gabriel graph 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luiz C. B. Torres
    • 1
  • André P. Lemos
    • 1
  • Cristiano L. Castro
    • 1
  • Antônio P. Braga
    • 1
  1. 1.Department of Electronics EngineeringFederal University of Minas GeraisBelo HorizonteBrazil

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