Dynamic Network Representation Based on Latent Factorization of Tensors - Couverture souple
Livre 25 sur 60: SpringerBriefs in Computer Science
Synopsis
The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Hao Wu received a Ph.D. degree in Computer Science from the University of Chinese Academy of Sciences, Beijing, China, in 2022. He is currently an Associate Professor of Data Science with the College of Computer and Information Science, Southwest University, Chongqing, China. His research interests include big data analytics and tensor methods.
Xuke Wu is currently pursuing a Ph.D. degree from the College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China. His current research interests include data mining and intelligent transportation systems.
Xin Luo received a Ph.D. degree in computer science from Beihang University, Beijing, China, in 2011. He is currently a Professor of Data Science and Computational Intelligence with the College of Computer and Information Science, Southwest University, Chongqing, China. He has authored or coauthored over 200 papers (including over 90 IEEE Transactions papers) in the areas of his interests. His research interests include big data analysis and intelligent control.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Autres éditions populaires du même titre
Résultats de recherche pour Dynamic Network Representation Based on Latent Factorization...
Image d'archives
Dynamic Network Representation Based on Latent Factorization of Tensors (SpringerBriefs in Computer Science)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9789811989339_new
Acheter neuf
EUR 53,61
Frais de port :
EUR 13,67
De Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
Dynamic Network Representation Based on Latent Factorization of Tensors (SpringerBriefs in Computer Science)
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. 1st ed. 2023 edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26396349349
Acheter neuf
EUR 72,14
Frais de port :
EUR 3,44
Vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
Dynamic Network Representation Based on Latent Factorization of Tensors
Edité par
Springer Nature Singapore Mrz 2023, 2023
ISBN 10 : 9811989338
ISBN 13 : 9789811989339
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes' various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency. 88 pp. Englisch. N° de réf. du vendeur 9789811989339
Acheter neuf
EUR 53,49
Frais de port :
EUR 23
De Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image d'archives
Dynamic Network Representation Based on Latent Factorization of Tensors (SpringerBriefs in Computer Science)
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur 401109114
Acheter neuf
EUR 75,64
Frais de port :
EUR 7,42
De Royaume-Uni vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image d'archives
Dynamic Network Representation Based on Latent Factorization of Tensors (SpringerBriefs in Computer Science)
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18396349359
Acheter neuf
EUR 76,72
Frais de port :
EUR 9,95
De Allemagne vers Etats-Unis
Quantité disponible : 4 disponible(s)
Image d'archives
Dynamic Network Representation Based on Latent Factorization of Tensors
Edité par
Springer-Nature New York Inc, 2023
ISBN 10 : 9811989338
ISBN 13 : 9789811989339
Neuf
Paperback
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : Brand New. 88 pages. 9.25x6.10x0.19 inches. In Stock. N° de réf. du vendeur x-9811989338
Acheter neuf
EUR 75,32
Frais de port :
EUR 11,41
De Royaume-Uni vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Dynamic Network Representation Based on Latent Factorization of Tensors
Edité par
Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10 : 9811989338
ISBN 13 : 9789811989339
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity,. N° de réf. du vendeur 757485768
Acheter neuf
EUR 48,37
Frais de port :
EUR 48,99
De Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Dynamic Network Representation Based on Latent Factorization of Tensors
Edité par
Springer Nature Singapore, Springer Nature Singapore Mär 2023, 2023
ISBN 10 : 9811989338
ISBN 13 : 9789811989339
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes¿ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 88 pp. Englisch. N° de réf. du vendeur 9789811989339
Acheter neuf
EUR 53,49
Frais de port :
EUR 60
De Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Dynamic Network Representation Based on Latent Factorization of Tensors
Edité par
Springer, Springer Nature Singapore, 2023
ISBN 10 : 9811989338
ISBN 13 : 9789811989339
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes' various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency. N° de réf. du vendeur 9789811989339
Acheter neuf
EUR 57,68
Frais de port :
EUR 60,75
De Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Dynamic Network Representation Based on Latent Factorization of Tensors
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Dynamic Network Representation Based on Latent Factorization of Tensors | Hao Wu (u. a.) | Taschenbuch | viii | Englisch | 2023 | Springer | EAN 9789811989339 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 125801260
Acheter neuf
EUR 50,95
Frais de port :
EUR 70
De Allemagne vers Etats-Unis
Quantité disponible : 5 disponible(s)