Full text
|
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (879kB) | Preview |
Benjumeda Barquita, Marco Alberto and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2016). Learning Bayesian networks with low inference complexity. "Progress in Artificial Intelligence", v. 5 (n. 1); pp. 15-26. ISSN 2192-6360. https://doi.org/10.1007/s13748-015-0070-0.
Title: | Learning Bayesian networks with low inference complexity |
---|---|
Author/s: |
|
Item Type: | Article |
Título de Revista/Publicación: | Progress in Artificial Intelligence |
Date: | 2016 |
ISSN: | 2192-6360 |
Volume: | 5 |
Subjects: | |
Freetext Keywords: | Probabilistic graphical models; Bayesian networks; Arithmetic circuits; Network polynomials; Structure learning; Thin models |
Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
|
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (879kB) | Preview |
One of the main research topics in machine learning nowadays is the improvement of the inference and learning processes in probabilistic graphical models. Traditionally, inference and learning have been treated separately, but given that the structure of the model conditions the inference complexity, most learning methods will sometimes produce inefficient inference models. In this paper we propose a framework for learning low inference complexity Bayesian networks. For that, we use a representation of the network factorization that allows efficiently evaluating an upper bound in the inference complexity of each model during the learning process. Experimental results show that the proposed methods obtain tractable models that improve the accuracy of the predictions provided by approximate inference in models obtained with a well-known Bayesian network learner.
Type | Code | Acronym | Leader | Title |
---|---|---|---|---|
Government of Spain | TIN2013-41592-P | Unspecified | Universidad Politécnica de Madrid | Aprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering |
Madrid Regional Government | S2013/ICE-2845 | CASI - CAM | Aníbal Ramón Figueiras Vidal | Conceptos y Aplicaciones de los Sistemas Inteligentes |
FP7 | 604102 | HBP | ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE | The Human Brain Project |
Government of Spain | C080020-09 | Unspecified | Unspecified | Cajal Blue Brain |
Item ID: | 41178 |
---|---|
DC Identifier: | https://oa.upm.es/41178/ |
OAI Identifier: | oai:oa.upm.es:41178 |
DOI: | 10.1007/s13748-015-0070-0 |
Official URL: | https://doi.org/10.1007/s13748-015-0070-0 |
Deposited by: | Memoria Investigacion |
Deposited on: | 20 Oct 2016 10:10 |
Last Modified: | 14 Mar 2019 16:23 |