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Memorias de investigación
Otras publicaciones:
Decision boundary for discrete Bayesian networks classifiers
Año:2014
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V-structures in the predictor sub-graph, we are also able to prove that this family of polynomials does in- deed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.
Internacional
Si
Entidad
Lugar
Páginas
23
Referencia/URL
http://oa.upm.es/26003/
Tipo de publicación
Technical Report TR:UPM-ESTIINF/DIA/2014-1, Universidad Politécnica de Madrid
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Gherardo Varando (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • 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
  • Departamento: Inteligencia Artificial
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