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Título

TAN Classifiers Based on Decomposable Distributions

Autor Cerquides, Jesus; Lopez de Mantaras, Ramon
Palabras clave Artificial Intelligence
Bayesian networks classifiers
Naive Bayes
Tree augmented naive Bayes
Decomposable distributions
Bayesian model averaging
Fecha de publicación 2005
EditorSpringer
Citación Machine Learning, 2005, 59 (3): 323-354
ResumenIn this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN's, we can compute the exact Bayesian model averaging over TAN structures and parameters in polynomial time. Furthermore, we prove that the k-maximum a posteriori (MAP) TAN structures can also be computed in polynomial time. We use these results to correct minor errors in Meila & Jaakkola (2000a) and to construct several TAN based classifiers provide consistently better predictions over Irvine datasets and artificially generated data than TAN based classifiers proposed in the literature.
Descripción The original publication is available at www.springerlink.com
URI http://hdl.handle.net/10261/3019
ISSN0885-6125
Aparece en las colecciones: (IIIA) Artículos
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