InforLorV4, Hal, Corpus, bibRecord, 002A96

Inference Bayesian Network for Multi-topographic neural network communication: a case study in documentary data

Identifieur interne : 002A96 ( Hal/Corpus ); précédent : 002A95; suivant : 002A97

Inference Bayesian Network for Multi-topographic neural network communication: a case study in documentary data

Auteurs : Shadi Al Shehabi ; Jean-Charles Lamirel

Source :

RBID : Hal:inria-00099927

Descripteurs français

mix :
- bayesian network, multiview, multivue, méthode neuronale, neural method, réseau bayésien.

Abstract

In this paper we present an original approach consisting in assimilating the behavior of the MultiSOM model, whose core model represents a significant extension of the classical Kohonen SOM model, to the one model of a Bayesian inference network. This approach is used both for validating the MultiSOM inter-map communication principles and for enhancing the accuracy of the probabilistic correlation computation mode that is already provided by the model In a complementary way, our approach also led us to prove that a neural multi-map model provided with unsupervised learning might well behave as a Bayesian inference network in which the estimation of posterior probabilities becomes a simple process only using prior similarity measures.

Url:

https://hal.inria.fr/inria-00099927

Links to Exploration step

Hal:inria-00099927

Le document en format XML

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<front><div type="abstract" xml:lang="en">In this paper we present an original approach consisting in assimilating the behavior of the MultiSOM model, whose core model represents a significant extension of the classical Kohonen SOM model, to the one model of a Bayesian inference network. This approach is used both for validating the MultiSOM inter-map communication principles and for enhancing the accuracy of the probabilistic correlation computation mode that is already provided by the model In a complementary way, our approach also led us to prove that a neural multi-map model provided with unsupervised learning might well behave as a Bayesian inference network in which the estimation of posterior probabilities becomes a simple process only using prior similarity measures.</div>
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<idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno>
<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
<idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Inference Bayesian Network for Multi-topographic neural network communication: a case study in documentary data</title>
<author role="aut"><persName><forename type="first">Shadi</forename>
<surname>Al Shehabi</surname>
</persName>
<email>alshehab@loria.fr</email>
<idno type="halAuthorId">130979</idno>
<orgName ref="#struct-300009"></orgName>
<affiliation ref="#struct-2348"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Jean-Charles</forename>
<surname>Lamirel</surname>
</persName>
<idno type="idHal">jean-charles-lamirel</idno>
<idno type="halAuthorId">131178</idno>
<orgName ref="#struct-300009"></orgName>
<affiliation ref="#struct-2348"></affiliation>
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<monogr><idno type="localRef">A04-R-482 || al_shehabi04a</idno>
<meeting><title>International Conference on Information and Communication Technologies: from Theory to Applications - ICTTA 2004</title>
<date type="start">2004</date>
<settlement>Damascus, Syria</settlement>
</meeting>
<imprint><biblScope unit="pp">6 p</biblScope>
<date type="datePub">2004</date>
</imprint>
</monogr>
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<profileDesc><langUsage><language ident="en">English</language>
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<textClass><keywords scheme="author"><term xml:lang="fr">méthode neuronale</term>
<term xml:lang="fr">bayesian network</term>
<term xml:lang="fr">neural method</term>
<term xml:lang="fr">multiview</term>
<term xml:lang="fr">multivue</term>
<term xml:lang="fr">réseau bayésien</term>
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<abstract xml:lang="en">In this paper we present an original approach consisting in assimilating the behavior of the MultiSOM model, whose core model represents a significant extension of the classical Kohonen SOM model, to the one model of a Bayesian inference network. This approach is used both for validating the MultiSOM inter-map communication principles and for enhancing the accuracy of the probabilistic correlation computation mode that is already provided by the model In a complementary way, our approach also led us to prove that a neural multi-map model provided with unsupervised learning might well behave as a Bayesian inference network in which the estimation of posterior probabilities becomes a simple process only using prior similarity measures.</abstract>
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Inference Bayesian Network for Multi-topographic neural network communication: a case study in documentary data

Inference Bayesian Network for Multi-topographic neural network communication: a case study in documentary data

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