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Título:
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Probabilistic conditional independence: a similarity-based measure and its application to causal network learning
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Autor/a:
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Sangüesa i Sole, Ramon; Cabós, Joan; Cortés García, Claudio Ulises
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
Abstract:
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A new definition for similarity between possibility distributions is
introduced
and discussed as a basis for detecting dependence between variables by
measuring the similarity degree of their respective distributions.
This new definition is used to detect conditional independence
relations in possibility
distributions derived from data. This is the basis for a new hybrid
algorithm for recovering possibilistic causal networks. The algorithm
POSSCAUSE is presented and its applications discussed and compared
with analogous developments in possibilistic and probabilistic causal
networks learning. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Similarity -Possibility distributions -POSSCAUSE |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Informe |
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