Categorization in a Hopfield network trained with weighted examples : extensive number of concepts
dc.contributor.author | Costa, Rogerio Adeodato Lima | pt_BR |
dc.contributor.author | Theumann, Alba Graciela Rivas de | pt_BR |
dc.date.accessioned | 2014-09-24T02:12:15Z | pt_BR |
dc.date.issued | 2000 | pt_BR |
dc.identifier.issn | 1063-651X | pt_BR |
dc.identifier.uri | http://hdl.handle.net/10183/103710 | pt_BR |
dc.description.abstract | We consider the categorization problem in a Hopfield network with an extensive number of concepts p =αN and trained with s examples of weight λτ, T=1, . . . ,s in the presence of synaptic noise represented by a dimensionless ‘‘temperature’’ T. We find that the retrieval capacity of an example with weight λ₁, and the corresponding categorization error, depend also on the arithmetic mean λm of the other weights. The categorization process is similar to that in a network trained with Hebb’s rule, but for λ₁/λm>1 the retrieval phase is enhanced. We present the phase diagram in the T-α plane, together with the de Almeida–Thouless line of instability. The phase diagrams in the α-s plane are discussed in the absence of synaptic noise and several values of the correlation parameter b. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | pt_BR |
dc.relation.ispartof | Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. Melville. Vol. 61, no. 5B (May 2000), p. 4860-4865 | pt_BR |
dc.rights | Open Access | en |
dc.subject | Fenomenos criticos | pt_BR |
dc.subject | Redes neurais de hopfield | pt_BR |
dc.subject | Aprendendo por exemplo | pt_BR |
dc.subject | Transições magnéticas | pt_BR |
dc.title | Categorization in a Hopfield network trained with weighted examples : extensive number of concepts | pt_BR |
dc.type | Artigo de periódico | pt_BR |
dc.identifier.nrb | 000275730 | pt_BR |
dc.type.origin | Estrangeiro | pt_BR |
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