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• W2022361797 startingPage "259" @default.
• W2022361797 abstract "The method presented here is aimed to a direct fast setting of the parameters of a RBF network for function approximation. It is based on a hierarchical gridding of the input space; additional layers of Gaussians at lower scales are added where the residual error is higher. The number of the Gaussians of each layer and their variance are computed from considerations grounded in the linear filtering theory. The weight of each Gaussian is estimated through a maximum a posteriori estimate carried out locally on a sub-set of the data points. The method shows a high accuracy in the reconstruction, it can deal with non-evenly spaced data points and can be fully parallelizable. Results on the reconstruction of both synthetic and real data are presented and discussed." @default.
• W2022361797 created "2016-06-24" @default.
• W2022361797 creator A5001947622 @default.
• W2022361797 creator A5063632316 @default.
• W2022361797 date "1998-04-01" @default.
• W2022361797 modified "2023-09-25" @default.
• W2022361797 title "Hierarchical RBF networks and local parameters estimate" @default.
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• W2022361797 doi "https://doi.org/10.1016/s0925-2312(97)00094-5" @default.
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