Order Total (1 Item Items):
Shipping Destination:
Wu Xuke (10 results)
Author:
wu xuke
Refine with Advanced Search
Skip to main search results
Search filters
Product Type
- All Product Types
- Books (10)
- Magazines & Periodicals (No further results match this refinement)
- Comics (No further results match this refinement)
- Sheet Music (No further results match this refinement)
- Art, Prints & Posters (No further results match this refinement)
- Photographs (No further results match this refinement)
- Maps (No further results match this refinement)
- Manuscripts & Paper Collectibles (No further results match this refinement)
Condition Learn more
- New (9)
- As New, Fine or Near Fine (No further results match this refinement)
- Very Good or Good (No further results match this refinement)
- Fair or Poor (No further results match this refinement)
- As Described (1)
Binding
- All Bindings
- Hardcover (No further results match this refinement)
- Softcover (10)
Collectible Attributes
- First Edition (No further results match this refinement)
- Signed (No further results match this refinement)
- Dust Jacket (No further results match this refinement)
- Seller-Supplied Images (1)
- Not Print on Demand (5)
Language (1)
Price
- Any Price
- Under US$ 25 (No further results match this refinement)
- US$ 25 to US$ 50
- Over US$ 50
Free Shipping
Seller Location
Seller Rating
-
Dynamic Network Representation Based on Latent Factorization of Tensors
Book 25 of 83: SpringerBriefs in Computer ScienceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 60.86
US$ 16.01 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
Dynamic Network Representation Based on Latent Factorization of Tensors (SpringerBriefs in Computer Science)
Book 27 of 83: SpringerBriefs in Computer ScienceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 62.87
US$ 16.01 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
Condition: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
-
Dynamic Network Representation Based on Latent Factorization of Tensors
Book 27 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer-Nature New York Inc, 2023
ISBN 10: 9811989338 ISBN 13: 9789811989339
US$ 87.23
US$ 13.36 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 88 pages. 9.25x6.10x0.19 inches. In Stock.
-
Dynamic Network Representation Based on Latent Factorization of Tensors
Book 27 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer Nature Singapore, 2023
ISBN 10: 9811989338 ISBN 13: 9789811989339
Condition: 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 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 nodesż 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.
-
Dynamic Network Representation Based on Latent Factorization of Tensors
Book 25 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer Nature B.V., 2023
ISBN 10: 9811989354 ISBN 13: 9789811989353
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
-
Dynamic Network Representation Based on Latent Factorization of Tensors
Book 25 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer Nature B.V., 2023
ISBN 10: 9811989354 ISBN 13: 9789811989353
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
US$ 63.52
US$ 4.40 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
-
-
-
Dynamic Network Representation Based on Latent Factorization of Tensors
Book 27 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811989338 ISBN 13: 9789811989339
Kartoniert / Broschiert. Condition: 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,.
