Abstract
We propose here to use a space-filling curve (SFC) as a tool to introduce a new metric in I d defined as a distance along the space-filling curve. This metric is to be used inside radial functions instead of the Euclidean or the Mahalanobis distance. This approach is equivalent to using SFC to pre-process the input data before training the RBF net. All the network tuning operations are performed in one dimension. Furthermore, we introduce a new method of computing the weights of linear output neuron, which is based on connection between RBF net and Nadaraya-Watson kernel regression estimators.
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Krzyżak, A., Skubalska-Rafajłowicz, E. (2004). Combining Space-Filling Curves and Radial Basis Function Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_30
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DOI: https://doi.org/10.1007/978-3-540-24844-6_30
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