Observatorio de I+D+i UPM

Memorias de investigación
Probabilistic Graphical Models and Evolutionary Computation
Áreas de investigación
  • Inteligencia artificial
This talk will review synergies between probabilistic graphical models and evolutionary computation. First, we will show how to use evolutionary computation in inference and in learning from data problems within probabilistic graphical models. The search for the maximum a posteriori assignment and the optimal triangulation of the moral graph will exemplify inference problems. Learning from data may be carried out both in the space of directed acyclic graphs and in the space of orderings. Second, we will illustrate how to use Bayesian networks and Gaussian networks for developing estimation of distribution algorithms in discrete and continuous domains, respectively. Third, recent advances will be presented, covering regularization methods for learning probabilistic graphical models from data, multi-label classification with multidimensional Bayesian networks classifiers and estimation of distribution algorithms based on copulas and Markov networks. The talk will finish with some challenging applications in bioinformatics and neuroscience.
Entidad relacionada
IEEE. 2010 World Congress on Computational Intelligence
Nacionalidad Entidad
Sin nacionalidad
Lugar del congreso
Barcelona, España
Esta actividad pertenece a memorias de investigación
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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