Categorization in a Hopfield network trained with weighted examples : extensive number of concepts
| Autor: | Costa, Rogerio Adeodato Lima, Theumann, Alba Graciela Rivas de |
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| Jazyk: | angličtina |
| Rok vydání: | 2000 |
| Předmět: | |
| Zdroj: | Repositório Institucional da UFRGS Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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