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Evolutionary q-Gaussian Radial Basis Functions for Binary-Classification

  • F. Fernández-Navarro
  • C. Hervás-Martínez
  • P. A. Gutiérrez
  • M. Cruz-Ramírez
  • M. Carbonero-Ruz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop + algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with eleven datasets, taken from the UCI repository is presented. The RBFNN with the q-Gaussian is compared to RBFNN with Gaussian, Cauchy and Inverse Multiquadratic RBFs.

Keywords

Hide Layer Radial Basis Function Hide Node Hybrid Algorithm Radial Basis Function Neural Network 
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 2010

Authors and Affiliations

  • F. Fernández-Navarro
    • 1
  • C. Hervás-Martínez
    • 1
  • P. A. Gutiérrez
    • 1
  • M. Cruz-Ramírez
    • 1
  • M. Carbonero-Ruz
    • 2
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCórdobaSpain
  2. 2.Department of Management and Quantitative MethodsETEACordobaSpain

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