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. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
González, J., Rojas, I., Pomares, H., & Ortega, J. (2003). Studying the convergence of the CFA algorithm. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2686, 550–557. https://doi.org/10.1007/3-540-44868-3_70
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