Decision boundary for discrete Bayesian network classifiers

Varando, Gherardo and Bielza Lozoya, Maria Concepcion and Larrañaga Múgica, Pedro (2014). Decision boundary for discrete Bayesian network classifiers. Monografía (Technical Report). E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Decision boundary for discrete Bayesian network classifiers
Author/s:
  • Varando, Gherardo
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Item Type: Monograph (Technical Report)
Date: 2014
Subjects:
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 in- deed 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 and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.

More information

Item ID: 26003
DC Identifier: http://oa.upm.es/26003/
OAI Identifier: oai:oa.upm.es:26003
Deposited by: Gherardo Varando
Deposited on: 16 May 2014 06:55
Last Modified: 30 Sep 2014 14:59
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