title: On the ability of neural networks to perform generalization by induction.
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.
authors.affiliation: Institute of Molecular Genetics, USSR Academy of Sciences, Moscow.
authors.forename: V V
authors.initials: VV
authors.lastname: Anshelevich
authors.forename: B R
authors.initials: BR
authors.lastname: Amirikian
authors.forename: A V
authors.initials: AV
authors.lastname: Lukashin
authors.forename: M D
authors.initials: MD
authors.lastname: Frank-Kamenetskii
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008959:Models, Neurological