Ontology type: schema:ScholarlyArticle
2001-01
AUTHORSPaul C. Kainen, Věra Kůrková, Andrew Vogt
ABSTRACTDevices such as neural networks typically approximate the elements of some function space X by elements of a nontrivial finite union M of finite-dimensional spaces. It is shown that if X=Lp(Ω) (1
‖f−M‖+Γ for some f in X. Thus, no continuous finite neural network approximation can be within any positive constant of a best approximation in the Lp-norm. More... »
PAGES143-147
http://scigraph.springernature.com/pub.10.1023/a:1010916406274
DOIhttp://dx.doi.org/10.1023/a:1010916406274
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