Artículo
Learning Markov Network Structures Constrained by Context-Specific Independences
Fecha de publicación:
12/2014
Editorial:
World Scientific
Revista:
International Journal On Artificial Intelligence Tools
ISSN:
0218-2130
e-ISSN:
1793-6349
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
This work focuses on learning the structure of Markov networks from data. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and numerical weights, where the structure describes independences that hold in the distribution. Depending on which is the goal of structure learning, learning algorithms can be divided into: density estimation algorithms, where structure is learned for answering inference queries; and knowledge discovery algorithms, where structure is learned for describing independences qualitatively. The latter algorithms present an important limitation for describing independences because they use a single graph; a coarse grain structure representation which cannot represent flexible independences. For instance, context-specific independences cannot be described by a single graph. To overcome this limitation, this work proposes a new alternative representation named canonical model as well as the CSPC algorithm; a novel knowledge discovery algorithm for learning canonical models by using context-specific independences as constraints. On an extensive empirical evaluation, CSPC learns more accurate structures than state-of-the-art density estimation and knowledge discovery algorithms. Moreover, for answering inference queries, our approach obtains competitive results against density estimation algorithms, significantly outperforming knowledge discovery algorithms.
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Articulos(CCT - MENDOZA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Citación
Bromberg, Facundo; Schluter, Federico Enrique Adolfo; Edera, Alejandro; Learning Markov Network Structures Constrained by Context-Specific Independences; World Scientific; International Journal On Artificial Intelligence Tools; 23; 6; 12-2014; 1-43
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