Studying the Convergence of the CFA Algorithm

  • Jesús González
  • Ignacio Rojas
  • Héctor Pomares
  • Julio Ortega
Conference paper

DOI: 10.1007/3-540-44868-3_70

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2686)
Cite this paper as:
González J., Rojas I., Pomares H., Ortega J. (2003) Studying the Convergence of the CFA Algorithm. In: Mira J., Álvarez J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg

Abstract

This paper studies the convergence properties of the previously proposed CFA (Clustering for Function Approximation) algorithm and compares its behavior with other input-output clustering techniques also designed for approximation problems. The results obtained show that CFA is able to obtain an initial configuration from which an approximator can improve its performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jesús González
    • 1
  • Ignacio Rojas
    • 1
  • Héctor Pomares
    • 1
  • Julio Ortega
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranada

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