๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Representation properties of multilayer feedforward networks

โœ Scribed by B. Moore; T. Poggio

Publisher
Elsevier Science
Year
1988
Tongue
English
Weight
90 KB
Volume
1
Category
Article
ISSN
0893-6080

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Approximation capabilities of multilayer
Approximation capabilities of multilayer feedforward networks
โœ Kurt Hornik ๐Ÿ“‚ Article ๐Ÿ“… 1991 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 700 KB

We show that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to Lp(ฮผ) performance criteria, for arbitrary finite input environment measures ฮผ, provided only that sufficien

Feedforward neural network's sensitivity
Feedforward neural network's sensitivity to input data representation
โœ Igor T. Podolak ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 434 KB

Neural networks can be used to develop solutions to problems which are strictly symbolic. A question arises how to represent symbols in terms of number vectors understandable to neural networks. Data representation used should promote good generalization and reduce simulation uncertainty of the resu

Multilayer feedforward networks with a n
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
โœ Moshe Leshno; Vladimir Ya. Lin; Allan Pinkus; Shimon Schocken ๐Ÿ“‚ Article ๐Ÿ“… 1993 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 527 KB

Several researchers characterized the activation fimction under which multilayer feedforward networks can act as universal approximators. We show that most of all the characterizations that were reported thus far in the literature are special cases of the following general result: A standard multila