Decision boundary for discrete Bayesian network classifiers

Varando, Gherardo and Bielza Lozoya, Maria Concepcion and Larrañaga Múgica, Pedro (2015). Decision boundary for discrete Bayesian network classifiers. "Journal of Machine Learning Research" (n. 16); pp. 2725-2749. ISSN 1533-7928.

Description

Title: Decision boundary for discrete Bayesian network classifiers
Author/s:
  • Varando, Gherardo
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Item Type: Article
Título de Revista/Publicación: Journal of Machine Learning Research
Date: December 2015
ISSN: 1533-7928
Subjects:
Freetext Keywords: Bayesian networks, supervised classification, decision boundary, polynomial threshold function, Lagrange basis
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: None

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Abstract

Bayesian network classifiers 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 classifiers. 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 specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure.

More information

Item ID: 40608
DC Identifier: http://oa.upm.es/40608/
OAI Identifier: oai:oa.upm.es:40608
Official URL: http://jmlr.org/papers/v16/varando15a.html
Deposited by: Archivo Digital UPM
Deposited on: 25 May 2016 06:57
Last Modified: 15 Jun 2016 09:09
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