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Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

The application of neural networks to domains involving prediction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. Traditional distances are inadequate to access the differences between the symbolic patterns. This work proposes the utilization of a statistically extracted distance measure in the context of Generalized Radial Basis Function (GRBF) networks. The main properties of the GRBF networks are retained in the new metric space. The regularization potential of these networks can be realized with this type of distance. Furthermore, the recent engineering of neural networks offers effective solutions for learning smooth functionals that lie on high dimensional spaces.

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Author information

Authors and Affiliations

  1. Department of Medical Physics, University of Patras, Patras, 26500, Greece

    Liviu Vladutu, Stergios Papadimitriou, Severina Mavroudi & Anastasios Bezerianos

  2. Department of Computer Engineering and Informatics, University of Patras, Greece

    Stergios Papadimitriou

Authors
  1. Liviu Vladutu
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  2. Stergios Papadimitriou
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  3. Severina Mavroudi
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  4. Anastasios Bezerianos
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Vladutu, L., Papadimitriou, S., Mavroudi, S., Bezerianos, A. (2001). Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_63

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  • DOI: https://doi.org/10.1007/3-540-45357-1_63

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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