Bayesian Network Structure Learning - Couverture souple

Gheisari, Soulmaz

9783330086913: Bayesian Network Structure Learning

Couverture souple

ISBN 10 : 3330086912 ISBN 13 : 9783330086913

Editeur : LAP Lambert Academic Publishing, 2018

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L'apprentissage des structures est un probl�me tr�s important dans le domaine des r�seaux bay�siens (BN). C'est aussi un domaine de recherche actif depuis plus de deux d�cennies, c'est pourquoi de nombreuses approches ont �t� propos�es afin de trouver une structure optimale � partir d'�chantillons d'entra�nement. Dans ce livre, nous introduisons bient�t les BNs et l'apprentissage de la structure ; puis, un algorithme bas� sur l'optimisation de l'essaim de particules (PSO) est propos� pour r�soudre le probl�me d'apprentissage de la structure BN. Dans l'algorithme propos�, qui a nomm� BNC-PSO (algorithme de construction de r�seau bay�sien utilisant PSO), l'insertion/suppression des bords est utilis�e pour que les particules aient la capacit� d'obtenir la solution optimale, tandis qu'une proc�dure de retrait de cycle est utilis�e pour emp�cher la g�n�ration de solutions non valides. Le th�or�me de la cha�ne de Markov est �galement utilis� pour prouver la convergence globale de l'algorithme propos�. Enfin, certaines exp�riences sont con�ues pour �valuer les performances de l'algorithme bas� sur le PSO propos�. Les r�sultats exp�rimentaux indiquent que BNC-PSO m�rite d'�tre �tudi� dans le domaine de la construction BN. Parall�lement, il peut augmenter consid�rablement de pr�s de 15 % des valeurs m�triques de notation, par rapport � d'autres algorithmes bas�s sur l'optimisation.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2018
Langue: anglais
ISBN 10: 3330086912
ISBN 13: 9783330086913
Reliure: Broch�
Nombre de pages: 64
Coordonn�es du fabricant: non disponible
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Bayesian Network Structure Learning

Soulmaz Gheisari

Edit� par LAP LAMBERT Academic Publishing Jun 2018, 2018

ISBN 10 : 3330086912 ISBN 13 : 9783330086913

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this book, we shortly introduce BNs and structure learning in them; then, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem. In the proposed algorithm, which named BNC-PSO (Bayesian Network Construction algorithm using PSO), edge inserting/deleting is employed to make the particles have the ability to achieve the optimal solution, while a cycle removing procedure is used to prevent the generation of invalid solutions. The theorem of Markov chain is also used to prove the global convergence of the proposed algorithm. Finally, some experiments are designed to evaluate the performance of the proposed PSO-based algorithm. Experimental results indicate that BNC-PSO is worthy of being studied in the field of BNs construction. Meanwhile, it can significantly increase nearly 15% in the scoring metric values, comparing with other optimization-based algorithms. 64 pp. Englisch. N� de r�f. du vendeur 9783330086913

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Bayesian Network Structure Learning

Soulmaz Gheisari

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 3330086912 ISBN 13 : 9783330086913

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gheisari SoulmazEducation: Science and Research University,Tehran, Iran. Faculty member, professor assistant of computer engineering in Islamic Azad University Pardis branch, Tehran, Iran. Also worked in Electricity Distribution Comp. N� de r�f. du vendeur 385708075

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Bayesian Network Structure Learning

Soulmaz Gheisari

Edit� par LAP LAMBERT Academic Publishing Jun 2018, 2018

ISBN 10 : 3330086912 ISBN 13 : 9783330086913

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this book, we shortly introduce BNs and structure learning in them; then, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem. In the proposed algorithm, which named BNC-PSO (Bayesian Network Construction algorithm using PSO), edge inserting/deleting is employed to make the particles have the ability to achieve the optimal solution, while a cycle removing procedure is used to prevent the generation of invalid solutions. The theorem of Markov chain is also used to prove the global convergence of the proposed algorithm. Finally, some experiments are designed to evaluate the performance of the proposed PSO-based algorithm. Experimental results indicate that BNC-PSO is worthy of being studied in the field of BNs construction. Meanwhile, it can significantly increase nearly 15% in the scoring metric values, comparing with other optimization-based algorithms.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 64 pp. Englisch. N� de r�f. du vendeur 9783330086913

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Bayesian Network Structure Learning

Soulmaz Gheisari

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 3330086912 ISBN 13 : 9783330086913

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this book, we shortly introduce BNs and structure learning in them; then, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem. In the proposed algorithm, which named BNC-PSO (Bayesian Network Construction algorithm using PSO), edge inserting/deleting is employed to make the particles have the ability to achieve the optimal solution, while a cycle removing procedure is used to prevent the generation of invalid solutions. The theorem of Markov chain is also used to prove the global convergence of the proposed algorithm. Finally, some experiments are designed to evaluate the performance of the proposed PSO-based algorithm. Experimental results indicate that BNC-PSO is worthy of being studied in the field of BNs construction. Meanwhile, it can significantly increase nearly 15% in the scoring metric values, comparing with other optimization-based algorithms. N� de r�f. du vendeur 9783330086913

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