Optimally adapted multistate neural networks trained with noise
The principle of adaptation in a noisy retrieval environment is extended here to a diluted attractor neural network of Q-state neurons trained with noisy data. The network is adapted to an appropriate noisy training overlap and training activity, which are determined self-consistently by the optimiz...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 1999 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Sul (UFRGS) |
| Repositorio: | Repositório Institucional da UFRGS |
| Idioma: | inglés |
| OAI Identifier: | oai:www.lume.ufrgs.br:10183/103647 |
| Acceso en línea: | http://hdl.handle.net/10183/103647 |
| Access Level: | acceso abierto |
| Palabra clave: | Física estatística Redes neurais Biofísica Transformacoes de ordem-desordem |
| Sumario: | The principle of adaptation in a noisy retrieval environment is extended here to a diluted attractor neural network of Q-state neurons trained with noisy data. The network is adapted to an appropriate noisy training overlap and training activity, which are determined self-consistently by the optimized retrieval attractor overlap and activity. The optimized storage capacity and the corresponding retriever overlap are considerably enhanced by an adequate threshold in the states. Explicit results for improved optimal performance and new retriever phase diagrams are obtained for Q=3 and Q=4, with coexisting phases over a wide range of thresholds. Most of the interesting results are stable to replica-symmetry-breaking fluctuations. |
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