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Designing RBFNNs Using Prototype Selection

  • Ana Cecilia Tenorio-González
  • José Fco. Martínez-Trinidad
  • Jesús Ariel Carrasco-Ochoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

Performance and accuracy of a neural network are strongly related to its design. Designing a neural network involves topology (number of neurons, number of layers, number of synapses between layers, etc.), training synapse weights, and parameter selection. Radial basis function neural networks (RBFNNs) could additionally require some other parameters, for example, the means and standard deviations if the activation function of neurons in the hidden layer is a Gaussian function. Commonly, Genetic Algorithms and Evolution Strategies have been used for automatically designing RBFNNs In this work, the use of prototype selection methods for designing a RBFNN is proposed and studied. Experimental results show the viability of designing RBFNNs using prototype selection.

Keywords

Neural networks RBFNN prototype selection supervised classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ana Cecilia Tenorio-González
    • 1
  • José Fco. Martínez-Trinidad
    • 1
  • Jesús Ariel Carrasco-Ochoa
    • 1
  1. 1.Optics and ElectronicsNational Institute for AstrophysicsPueblaMéxico

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