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Experiments with domain knowledge in unsupervised learning: Using and revising theories

Autor
Bejar, J.; Cortes, U.
Tipus d'activitat
Article en revista
Revista
Computación y sistemas: revista iberoamericana de computación
Data de publicació
1998-01
Volum
1
Número
3
Pàgina inicial
136
Pàgina final
144
Resum
Using domain knowledge in unsupervised learning has shown to be a useful strategy when the set of examples of a given domain has not an evident structure or presents some level of noise. This background knowledge can be expressed as a set of classification rules and introduced as a semantic bias during the learning process. In this work we present some experiments on the use of partial domain knowledge with the tool LINNEO+, a conceptual clustering algorithm. The domain knowledge (or domain theo...
Paraules clau
Knowledge Acquisition, Domain Theory, Ill-structured Domains, Clustering Methods
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants