9789811989339 - dynamic network representation based on latent factorization of tensors (springerbriefs in computer science) von wu, hao; wu, xuke; luo, xin (6 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2023
Serie: SpringerBriefs in Computer Science, Buch 27 von 60. Buch 27 von 60 - SpringerBriefs in Computer Science
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 55,22
EUR 14,06 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2023
Serie: SpringerBriefs in Computer Science, Buch 27 von 60. Buch 27 von 60 - SpringerBriefs in Computer Science
- Softcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 76,68
EUR 11,74 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Paperback. Zustand: Brand New. 88 pages. 9.25x6.10x0.19 inches. In Stock.
Sprache: Englisch
Verlag: Springer, 2023
Serie: SpringerBriefs in Computer Science, Buch 27 von 60. Buch 27 von 60 - SpringerBriefs in Computer Science
- Softcover
Anbieter: Kennys Bookstore, Olney, MD, USAKennys Bookstore
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 84,15
EUR 9,20 VersandVersand innerhalb von USAAnzahl: 15 verfügbar
Zustand: New.
Sprache: Englisch
Verlag: Springer, Springer, 2023
Serie: SpringerBriefs in Computer Science, Buch 27 von 60. Buch 27 von 60 - SpringerBriefs in Computer Science
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 58,39
EUR 60,75 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Taschenbuch. Zustand: 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 ent…ity, 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.
Weitere BilderSprache: Englisch
Verlag: Springer, 2023
Serie: SpringerBriefs in Computer Science, Buch 27 von 60. Buch 27 von 60 - SpringerBriefs in Computer Science
- Softcover
Anbieter: preigu, Osnabrück, Deutschlandpreigu
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 50,40
EUR 70,00 VersandVersand von Deutschland nach USAAnzahl: 5 verfügbar
Taschenbuch. Zustand: 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 | Anbie…ter: preigu.
Sprache: Englisch
Verlag: Springer Nature Singapore, 2023
Serie: SpringerBriefs in Computer Science, Buch 27 von 60. Buch 27 von 60 - SpringerBriefs in Computer Science
- Softcover
Anbieter: Buchpark, Trebbin, DeutschlandBuchpark
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht
EUR 33,87
EUR 105,00 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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 speci…fied 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.
