Mensaje de error

  • Unable to create CTools CSS cache directory. Check the permissions on your files directory.
  • Unable to create CTools CSS cache directory. Check the permissions on your files directory.
  • El archivo especificado temporary://fileRx6VJc no se pudo copiar, porque el directorio de destino no está configurado correctamente. La causa puede ser un problema de permisos en el directorio o los archivos. Hay más información disponible en el registro del sistema.

The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

TítuloThe Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
Publication TypeConference Paper
Year of Publication2014
AuthorsEdera A, Strappa Y, Bromberg F
Conference Name14th edition of the Ibero-American Conference on Artificial Intelligence
Date Published11/2014
PublisherLecture Notes in Computer Science/Lecture Notes in Artificial Intelligence LNCS/LNAI series
Conference LocationSantiago de Chile
ISBN Number978-3-319-12027-0
Abstract

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.

URLhttp://www.springer.com/computer/ai/book/978-3-319-12026-3
Miembros del DHARMa que son autores:: 
Peer reviewed?: 
1
Internacional?: 
1