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Training RBFs Networks: A Comparison Among Supervised and Not Supervised Algorithms

  • Conference paper
Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4232))

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Abstract

In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neural networks. In particular we compare the classical training which consist of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations.

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Author information

Authors and Affiliations

  1. Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Avda. Sos Baynat s/n, C.P. 12071, Castellon, Spain

    Mercedes Fernández-Redondo, Joaquín Torres-Sospedra & Carlos Hernández-Espinosa

Authors
  1. Mercedes Fernández-Redondo
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  2. Joaquín Torres-Sospedra
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  3. Carlos Hernández-Espinosa
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Fernández-Redondo, M., Torres-Sospedra, J., Hernández-Espinosa, C. (2006). Training RBFs Networks: A Comparison Among Supervised and Not Supervised Algorithms. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_53

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  • DOI: https://doi.org/10.1007/11893028_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46479-2

  • Online ISBN: 978-3-540-46480-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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