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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© 2003 Springer-Verlag Berlin Heidelberg
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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. https://doi.org/10.1007/3-540-44868-3_70
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DOI: https://doi.org/10.1007/3-540-44868-3_70
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