2015
Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro
Decision boundary for discrete Bayesian network classifiers Artículo de revista
En: Journal of Machine Learning Research, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function
@article{Varando2015c,
title = {Decision boundary for discrete Bayesian network classifiers},
author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://cig.fi.upm.es/node/881 http://cig.fi.upm.es/articles/2015/Varando-2015-JMLR.pdf},
year = {2015},
date = {2015-01-01},
journal = {Journal of Machine Learning Research},
abstract = {Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure},
keywords = {Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function},
pubstate = {published},
tppubtype = {article}
}
Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure