Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/18685
Title: | Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination |
Author: | Romeo Val, August |
Keywords: | Física estadística Processos estocàstics Biofísica Statistical physics Stochastic processes Biophysics |
Issue Date: | 1993 |
Publisher: | The American Physical Society |
Abstract: | Stochastic learning processes for a specific feature detector are studied. This technique is applied to nonsmooth multilayer neural networks requested to perform a discrimination task of order 3 based on the ssT-block¿ssC-block problem. Our system proves to be capable of achieving perfect generalization, after presenting finite numbers of examples, by undergoing a phase transition. The corresponding annealed theory, which involves the Ising model under external field, shows good agreement with Monte Carlo simulations. |
Note: | Reproducció del document publicat a: http://dx.doi.org/10.1103/PhysRevE.47.2162 |
It is part of: | Physical Review E, 1993, vol. 47, núm. 3, p. 2162-2171 |
URI: | http://hdl.handle.net/2445/18685 |
Related resource: | http://dx.doi.org/10.1103/PhysRevE.47.2162 |
ISSN: | 1063-651X |
Appears in Collections: | Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació) |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.