A generaliser applied to SBL
In this work two algorithms have been developed: a generaliser and a hierarchy constructor. The generaliser receives as inputs the descriptions of some positive examples of a concept and it calculates their maximal conjunctive generalisation. This algorithm is an important part within the learning a...
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 1992 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/370354 |
| Acceso en línea: | https://hdl.handle.net/2117/370354 |
| Access Level: | acceso abierto |
| Palabra clave: | Graph algorithms Computer algorithms Algorismes de grafs Algorismes computacionals Àrees temàtiques de la UPC::Informàtica |
| Sumario: | In this work two algorithms have been developed: a generaliser and a hierarchy constructor. The generaliser receives as inputs the descriptions of some positive examples of a concept and it calculates their maximal conjunctive generalisation. This algorithm is an important part within the learning algorithm proposed by Gustavo Núñez, which is based on the SBL (Similitude Based Learning) paradigm. The hierarchy constructor receives the generalisations calculated by the first algorithm and integrates them within a graph structure, which expresses the relationships between the concepts described by the generalisations. Both algorithms are written in Common Lisp and have been tested using the blocks world domain, but the generaliser has been written in such a way that it can be used in any domain satisfying some restrictions. |
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