Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Año:2013
Áreas de investigación
  • Inteligencia artificial (redes neuronales, lógica borrosa, sistemas expertos, etc)
Datos
Descripción
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms. Evolutionary algorithms, as successful problem solvers, are promising candidates for this purpose. This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning.
Internacional
Si
JCR del ISI
Si
Título de la revista
Information Sciences
ISSN
0020-0255
Factor de impacto JCR
2,833
Información de impacto
Datos JCR del año 2011
Volumen
233
DOI
Número de revista
0
Desde la página
109
Hasta la página
125
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Roberto Santana Hermida (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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