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Classification with Synaptic Radial Basis Units

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Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence (IWANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

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Abstract

A new type of Multilayer network including certain class of Radial Basis Units (RBU), whose kernels are implemented at the synaptic level, is compared through simulations with the Multi-Layer Perceptron (MLP) in a classification problem with a high interference of class distributions. The simulations show that the new network gives error rates in the classification near those of the Optimum Bayesian Classifier (OBC), while MLP presents an inherent weakness for these classification tasks.

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Author information

Authors and Affiliations

  1. Dept. Ingeniería Electrónica & Comunicaciones, Centro Politécnico Superior, C/ Maria de Luna 3, 50015, Zaragoza, Spain

    J. David Buldain

Authors
  1. J. David Buldain
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Buldain, J.D. (2001). Classification with Synaptic Radial Basis Units. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_26

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  • DOI: https://doi.org/10.1007/3-540-45720-8_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

  • eBook Packages: Springer Book Archive

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