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Automatic Design of Binary W-Operators Using Artificial Feed-Forward Neural Networks Based on the Weighted Mean Square Error Cost Function

  • Marco Benalcázar
  • Marcel Brun
  • Virginia Ballarin
  • Isabel Passoni
  • Gustavo Meschino
  • Lucía Dai Pra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

One of the main issues concerning automatic design of W-operators is the one of generalization. Considering the designing of W-operators as a particular case of designing a pattern recognition system, in this work we propose a new approach for the automatic design of binary W-operators. This approach consists on a functional representation of the conditional probabilities for the whole set of patterns viewed by a given window, instead the values of the characteristic function. The estimation of its parameters is achieved by means of a nonlinear regression performed by an artificial feed-forward neural network based on a weighted mean square error cost function. Experimental results show that, for the applications presented in this work, the proposed approach leads to better results than one of the best existing methods of generalization within the family of W-operators, like is the case of pyramidal multiresolution.

Keywords

W-operators pattern recognition artificial neural network nonlinear regression weighted mean square error pyramidal multiresolution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marco Benalcázar
    • 1
    • 2
    • 3
  • Marcel Brun
    • 1
  • Virginia Ballarin
    • 1
  • Isabel Passoni
    • 4
  • Gustavo Meschino
    • 4
  • Lucía Dai Pra
    • 4
  1. 1.Grupo de Procesamiento de ImágenesUniversidad Nacional de Mar del PlataArgentina
  2. 2.Consejo Nacional de Investigaciones Científicas y TécnicasArgentina
  3. 3.Secretaría Nacional de Educación Superior Ciencia, Tecnología e InnovaciónEcuador
  4. 4.Laboratorio de BioingenieríaUniversidad Nacional de Mar del PlataArgentina

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