ISSN:
1432-0770
Source:
Springer Online Journal Archives 1860-2000
Topics:
Biology
,
Computer Science
,
Physics
Notes:
Abstract The ability of neural networks to perform generalization by induction is the ability to learn an algorithm without the benefit of complete information about it. We consider the properties of networks and algorithms that determine the efficiency of generalization. These properties are described in quantitative terms. The most effective generalization is shown to be achieved by networks with the least admissible capacity. General conclusions are illustrated by computer simulations for a three-layered neural network. We draw a quantitative comparison between the general equations and specific results reported here and elsewhere.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00204596
