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• W2228366952 abstract "Our aim is to propose a method for selecting a radial basis functions terms to be included into a neural net model. As it is frequently met in practice, we consider the case of a deficit in the admissible number of observations (learning sequence) in comparison with a much larger number of candidate terms. The proposed approach is based on a random sieve that aims at selecting only necessary RBF’s by a hierarchy of a large number of random mixing of candidate RBF’s and testing their significance. The results of simulations are also reported." @default.
• W2228366952 created "2016-06-24" @default.
• W2228366952 creator A5019629599 @default.
• W2228366952 creator A5064805184 @default.
• W2228366952 date "2013-01-01" @default.
• W2228366952 modified "2023-09-27" @default.
• W2228366952 title "Random Sieve Based on Projections for RBF Neural Net Structure Selection" @default.
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• W2228366952 doi "https://doi.org/10.1007/978-3-642-38658-9_18" @default.
• W2228366952 hasPublicationYear "2013" @default.
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