Observatorio de I+D+i UPM

Memorias de investigación
Cursos, seminarios y tutoriales:
Gaussian Networks, EDAs and Regularization
Año:2009
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
In this talk we will present two recent contributions within the field of regularization. Regularization techniques provide estimates for the linear regression coefficients solving the problems encountered in the "few samples and many variables" setting. The main idea is to shrink the coefficients to zero by imposing a penalty on their size. We will firstly review the main regularization techniques. We will then propose a method for the structure learning of Gaussian Bayesian networks. The search in an equivalence class search space is combined with regularization techniques, promoting a sparse network learning. Finally, a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms is presented. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization.
Internacional
Si
Nombre congreso
Seminario para el Department of Computer Science
Entidad organizadora
Faculties of Engineering, Science and Medicine
Nacionalidad Entidad
DINAMARCA
Lugar/Ciudad de impartición
Aalborg
Fecha inicio
29/05/2009
Fecha fin
29/05/2009
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Inteligencia Artificial
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