%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 thema:recurrent_neural_networks %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 = {2016-06-19T16:06:54.000+0200}, author = {Leshno, Moshe and Lin, Vladimir Ya. and Pinkus, Allan and Schocken, Shimon}, biburl = {https://www.bibsonomy.org/bibtex/2e6f068012f6a9b66631cec7b6a4ea749/ericrauch}, description = {dblp}, ee = {http://dx.doi.org/10.1016/S0893-6080(05)80131-5}, interhash = {28c2f34cb2d6d93e45062740e34ea852}, intrahash = {e6f068012f6a9b66631cec7b6a4ea749}, journal = {Neural Networks}, keywords = {thema:recurrent_neural_networks}, number = 6, pages = {861-867}, timestamp = {2016-06-19T16:06:54.000+0200}, 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 }