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Abstract
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| 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. | |
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International
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Entity
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Place
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Pages
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23 |
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Reference/URL
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http://oa.upm.es/26003/ |
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Publication type
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Technical Report TR:UPM-ESTIINF/DIA/2014-1, Universidad Politécnica de Madrid |