๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

On learning of Sigmoid Neural Networks

โœ Scribed by Kayvan Najarian

Publisher
John Wiley and Sons
Year
2001
Tongue
English
Weight
149 KB
Volume
6
Category
Article
ISSN
1076-2787

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Automatic learning in chaotic neural net
Automatic learning in chaotic neural networks
โœ Masataka Watanabe; Kazuyuki Aihara; Shunsuke Kondo ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 445 KB ๐Ÿ‘ 1 views
DESIGN AND LEARNING WITH CELLULAR NEURAL
DESIGN AND LEARNING WITH CELLULAR NEURAL NETWORKS
โœ NOSSEK, JOSEF A. ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 752 KB

The template coefficients (weights) of a CNN which will give a desired performance, can either be found by design or by learning. 'By design' means that the desired function to be performed can be translated into a set of local dynamic rules, while 'by learning' is based exclusively on pairs of inpu

Polynomial Bounds for VC Dimension of Si
Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks
โœ Marek Karpinski; Angus Macintyre ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 438 KB

We introduce a new method for proving explicit upper bounds on the VC dimension of general functional basis networks and prove as an application, for the first time, that the VC dimension of analog neural networks with the sigmoidal activation function \_( y)=1ร‚1+e & y is bounded by a quadratic poly