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Article

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1993

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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/18685

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

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http://dx.doi.org/10.1103/PhysRevE.47.2162

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.

Subject

Física estadística, Processos estocàstics, Biofísica

Subject (English)

Statistical physics, Stochastic processes, Biophysics

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Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)

Citation

ROMEO VAL, August. Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination. Physical Review E. 1993. Vol. 47, num. 3, pags. 2162-2171. ISSN 1063-651X. [consulted: 16 of August of 2026]. Available at: https://hdl.handle.net/2445/18685

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