%0 Journal Article
%1 journals/nn/LeshnoLPS93
%A Leshno, Moshe
%A Lin, Vladimir Ya.
%A Pinkus, Allan
%A Schocken, Shimon
%D 1993
%J Neural Networks
%K deep_learning final thema:double_dqn
%N 6
%P 861-867
%T Multilayer feedforward networks with a nonpolynomial activation function can approximate any function.
%U http://dblp.uni-trier.de/db/journals/nn/nn6.html#LeshnoLPS93
%V 6
@article{journals/nn/LeshnoLPS93,
added-at = {2019-12-09T10:15:49.000+0100},
author = {Leshno, Moshe and Lin, Vladimir Ya. and Pinkus, Allan and Schocken, Shimon},
biburl = {https://www.bibsonomy.org/bibtex/2e6f068012f6a9b66631cec7b6a4ea749/jan.hofmann1},
description = {dblp},
ee = {http://dx.doi.org/10.1016/S0893-6080(05)80131-5},
interhash = {28c2f34cb2d6d93e45062740e34ea852},
intrahash = {e6f068012f6a9b66631cec7b6a4ea749},
journal = {Neural Networks},
keywords = {deep_learning final thema:double_dqn},
number = 6,
pages = {861-867},
timestamp = {2019-12-09T10:15:49.000+0100},
title = {Multilayer feedforward networks with a nonpolynomial activation function can approximate any function.},
url = {http://dblp.uni-trier.de/db/journals/nn/nn6.html#LeshnoLPS93},
volume = 6,
year = 1993
}