Lazy Training of Radial Basis Neural Networks

  • José M. Valls
  • Inés M. Galván
  • Pedro Isasi
Conference paper

DOI: 10.1007/11840817_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)
Cite this paper as:
Valls J.M., Galván I.M., Isasi P. (2006) Lazy Training of Radial Basis Neural Networks. In: Kollias S.D., Stafylopatis A., Duch W., Oja E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg

Abstract

Usually, training data are not evenly distributed in the input space. This makes non-local methods, like Neural Networks, not very accurate in those cases. On the other hand, local methods have the problem of how to know which are the best examples for each test pattern. In this work, we present a way of performing a trade off between local and non-local methods. On one hand a Radial Basis Neural Network is used like learning algorithm, on the other hand a selection of the training patterns is used for each query. Moreover, the RBNN initialization algorithm has been modified in a deterministic way to eliminate any initial condition influence. Finally, the new method has been validated in two time series domains, an artificial and a real world one.

Keywords

Lazy Learning Local Learning Radial Basis Neural Networks Pattern Selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • José M. Valls
    • 1
  • Inés M. Galván
    • 1
  • Pedro Isasi
    • 1
  1. 1.Departamento de InformáticaUniversidad Carlos III de MadridLeganés (Madrid)Spain

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