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Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination

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dc.creator Romeo Val, August
dc.date 2011-07-07T12:50:25Z
dc.date 2011-07-07T12:50:25Z
dc.date 1993
dc.date.accessioned 2024-12-16T10:27:17Z
dc.date.available 2024-12-16T10:27:17Z
dc.identifier 1063-651X
dc.identifier http://hdl.handle.net/2445/18685
dc.identifier 73475
dc.identifier.uri http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/22195
dc.description 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.
dc.format 10 p.
dc.format application/pdf
dc.language eng
dc.publisher The American Physical Society
dc.relation Reproducció del document publicat a: http://dx.doi.org/10.1103/PhysRevE.47.2162
dc.relation Physical Review E, 1993, vol. 47, núm. 3, p. 2162-2171
dc.relation http://dx.doi.org/10.1103/PhysRevE.47.2162
dc.rights (c) The American Physical Society, 1993
dc.rights info:eu-repo/semantics/openAccess
dc.source Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)
dc.subject Física estadística
dc.subject Processos estocàstics
dc.subject Biofísica
dc.subject Statistical physics
dc.subject Stochastic processes
dc.subject Biophysics
dc.title Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination
dc.type info:eu-repo/semantics/article
dc.type info:eu-repo/semantics/publishedVersion


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