Semantic Representations in a Weightless Neural Network
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Semantic Representations in a Weightless Neural Network
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| Id. |
3972617 |
| Idioma |
PT
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| Titulo |
Semantic Representations in a Weightless Neural Network |
| Autor(es) |
Joel de Lima Pereira Castro Junior
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| Localización |
http://tede.ibict.br/tde_busca/arquivo.php?codArquivo=358
http://tede.ibict.br/tde_busca/arquivo.php?codArquivo=359
http://tede.ibict.br/tde_busca/arquivo.php?codArquivo=360
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| Versión |
1.0 |
| Estado |
Final
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| Descripción |
The objective of this thesis is to present and evaluate a way of instilling semantic knowledge into a ?weightless? neural model. The research developed uses neural state machines models (NSMM) to bind together the neural computational paradigm with knowledge representation paradigm. Starting from a critical analysis of existing models, it defines which generalisation capabilities are required by a neural statemachine in order to emulate hierarchical relationships needed by semantic representations. Several generalisation algorithms, which combine the required generalisation capabilities and the application of newly defined rules of similarity, are presented and discussed. This effort has driven the definition of a novel spreading algorithm which applies different rules of similarities to node partitions responsiblefor identifying specific hierarchical relationships. This allowed ?queries? can be posed to the neural state machine and be ?answered? according to their ?semantics?. Theacquired generalisation capability is such that it does not require that all possible inter-relationships, which can be derived from the Knowledge Base itself, are workedout and trained in advance.The performance of the proposed neural state machine in generalisation tasks was experimentally tested and involved the creation of a new measurement for the qualityof the results. Extensive testing and statistical analysis suggests that the proposed model is found to be robust with respect to variations both on the degree of connectivity and on the size of the training set.
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| Tipo |
PDF
PDF
PDF
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| Palabras clave |
CIENCIA DA COMPUTACAO
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| Tipo de recurso |
Electronic Thesis or Dissertation
Tese ou Dissertacao Eletronica
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| Tipo de Interactividad |
Expositivo
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| Nivel de Interactividad |
muy bajo
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| Audiencia |
Estudiante
Profesor
Autor
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| Estructura |
Atomic |
| Coste |
no
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| Copyright |
sí
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Liberar o conteúdo dos arquivos para acesso público |
| Formatos |
PDF
PDF
PDF
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| Requerimientos técnicos |
Browser: Any
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| Fecha de contribución |
08-may-2008 |
| Contacto |
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