Hangjun che (12 Ergebnisse)
Matrix Factorization for Multimedia Clustering : Models, Techniques, Optimization and Applications
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-fai; Pan, Baicheng
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2026
- Hardcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 122,24
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Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications (Computing and Networks)
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-Fai; Pan, Baicheng
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2026
- Hardcover
Anbieter: California Books, Miami, FL, USACalifornia Books
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EUR 124,89
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Zustand: New.
Matrix Factorization for Multimedia Clustering : Models, Techniques, Optimization and Applications
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-fai; Pan, Baicheng
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2026
- Hardcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 130,81
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Zustand: As New. Unread book in perfect condition.
Matrix Factorization for Multimedia Clustering
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-Fai; Pan, Baicheng
- Hardcover
Anbieter: PBShop.store US, Wood Dale, IL, USAPBShop.store US
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EUR 155,14
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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Matrix Factorization for Multimedia Clustering
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-Fai; Pan, Baicheng
- Hardcover
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes KönigreichPBShop.store UK
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EUR 150,12
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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Matrix Factorization for Multimedia Clustering : Models, Techniques, Optimization and Applications
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-fai; Pan, Baicheng
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2026
- Hardcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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EUR 137,78
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Zustand: As New. Unread book in perfect condition.
Matrix Factorization for Multimedia Clustering : Models, Techniques, Optimization and Applications
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-fai; Pan, Baicheng
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2026
- Hardcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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EUR 143,99
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Zustand: New.
Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications
Che, Hangjun/ Wang, Xin/ He, Xing/ Leung, Man-fai/ Pan, Baicheng
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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EUR 164,16
EUR 14,63 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Hardcover. Zustand: Brand New. 300 pages. 9.21x6.14 inches. In Stock.
Sprache: Englisch
Verlag: Institution Of Engineering & Technology Feb 2026, 2026
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 168,00
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Buch. Zustand: Neu. Neuware - Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data may exhi…bit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance. Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets.
Sprache: Englisch
Verlag: Institution of Engineering and Technology, Stevenage, 2026
- Hardcover
- Print-on-Demand
Anbieter: Grand Eagle Retail, Bensenville, IL, USAGrand Eagle Retail
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EUR 124,58
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Hardcover. Zustand: new. Hardcover. Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data ma…y exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets. This book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization in multimedia information processing. The authors present methods to address these challenges, examine popular regularization techniques, and explore the relationship between regularization and clustering performance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-Fai; Pan, Baicheng
Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2026
- Hardcover
- Print-on-Demand
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes KönigreichTHE SAINT BOOKSTORE
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 152,96
EUR 18,73 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Sprache: Englisch
Verlag: Institution of Engineering and Technology, Stevenage, 2025
- Hardcover
- Print-on-Demand
Anbieter: CitiRetail, Stevenage, Vereinigtes KönigreichCitiRetail
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 158,48
EUR 43,30 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 1 verfügbar
Hardcover. Zustand: new. Hardcover. Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data ma…y exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets. This book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization in multimedia information processing. The authors present methods to address these challenges, examine popular regularization techniques, and explore the relationship between regularization and clustering performance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
