Observatorio de I+D+i UPM

Memorias de investigación
Tesis:
Supervised classification in continuous domains with Bayesian networks
Año:2010
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Supervised classification in mixed domains with probabilistic graphical models: we have adapted a set of algorithms taken from Bayesian multinomial networks to conditional Gaussian networks. We also have proposed novel classifier induction algorithms based on the particularities of conditional Gaussian networks. Moreover, we have proposed the novel kernel based Bayesian network paradigm which extends the idea of flexible naive Bayes breaking with the parametric assumptions. In addition, we have adapted some of the algorithms proposed for Bayesian multinomial networks to this novel paradigm. In order to present the kernel based Bayesian network paradigm, the mixed Gaussian kernel distribution is introduced
Internacional
Si
ISBN
Tipo de Tesis
Doctoral
Calificación
Sobresaliente cum laude
Fecha
21/05/2010
Esta actividad pertenece a memorias de investigación
Participantes
  • Director: Pedro Maria Larrañaga Mugica (UPM)
  • Director: Iñaki Inza (Universidad del País Vasco)
  • Doctorando: Aritz Pérez Martínez (Universidad del País Vasco)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
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