๐”– Bobbio Scriptorium
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Neural networks and approximation theory

โœ Scribed by H.N. Mhaskar


Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
192 KB
Volume
9
Category
Article
ISSN
0893-6080

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๐Ÿ“œ SIMILAR VOLUMES


Random Approximants and Neural Networks
โœ Y. Makovoz ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 492 KB

Let D be a set with a probability measure +, +(D)=1, and let K be a compact subset of L q (D, +), where the infimum is taken over all g n of the form g n = n i=1 a i , i , with arbitrary , i # K and a i # R. It is shown that for f # conv(K \_ (&K )), under some mild restrictions, \ n ( f, K ) C q =

Uniform Approximation by Neural Networks
โœ Y. Makovoz ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 262 KB

Let D/R d be a compact set and let 8 be a uniformly bounded set of D ร„ R functions. For a given real-valued function f defined on D and a given natural number n, we are looking for a good uniform approximation to f of the form n i=1 a i , i , with , i # 8, a i # R. Two main cases are considered: (1)

Approximation theory and feedforward net
โœ Edward K. Blum; Leong Kwan Li ๐Ÿ“‚ Article ๐Ÿ“… 1991 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 650 KB

Api~rosirnation of' reul fmctiom /I!, f&d~orwcrrd rwtwork.s of' thr ~rsurrl kind is sI~o~'rr to h lxr.scd 011 the fkntlwnet~tml principle of approxitnrrtion hx pip~pwisc-c,orl.sttr,lr fhctiotl.s.