Some circumscriptional thoughts on SBL

We illustrate how some of the Similitude Based Learning (SBL) paradigms can be reformulated using some logical formalisms as circumscription, predicate completion and the close world assumption. Our approach shows that is possible to use these logical tools in order to obtain a formal unified vision...

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Detalles Bibliográficos
Autores: Núñez Esquer, Gustavo, Cortés García, Claudio Ulises|||0000-0003-0192-3096
Tipo de recurso: informe técnico
Fecha de publicación:1989
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:2117/190354
Acceso en línea:https://hdl.handle.net/2117/190354
Access Level:acceso abierto
Palabra clave:Machine learning
Similitude Based Learning
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Àrees temàtiques de la UPC::Informàtica
Descripción
Sumario:We illustrate how some of the Similitude Based Learning (SBL) paradigms can be reformulated using some logical formalisms as circumscription, predicate completion and the close world assumption. Our approach shows that is possible to use these logical tools in order to obtain a formal unified vision of SBL paradigms, and it also suggests some kind of improvements on the current implementations. We introduce a hitherto unmentioned direct link between machine learning and circumscription. We believe that this new framework and the obtained results are of practical interest.