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

# metadata.dc.description.abstract[].value
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.

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

# metadata.dc.title[].value
Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination