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Prior knowledge for learning networks in non-probabilistic settings

Autor
Sangüesa, R.; Cortes, U.
Tipus d'activitat
Article en revista
Revista
International journal of approximate reasoning
Data de publicació
2000-04
Volum
24
Número
1
Pàgina inicial
103
Pàgina final
120
DOI
https://doi.org/10.1016/S0888-613X(99)00046-8 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0888613X99000468 Obrir en finestra nova
Resum
Current learning methods for general causal networks are basically data-driven. Exploration of the search space is made by resorting to some quality measure of prospective solutions. This measure is usually based on statistical assumptions. We discuss the interest of adopting a different point of view closer to machine learning techniques. Our main point is the convenience of using prior knowledge when it is available. We identify several sources of prior knowledge and define their role in the l...
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants