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Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination
Romeo Val, August
Universitat de Barcelona
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.
Física estadística
Processos estocàstics
Statistical physics
Stochastic processes
(c) The American Physical Society, 1993
The American Physical Society

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