Isbn: 9783031335594 - multi-aspect learning: methods and applications: 242 (11 risultati)

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Hardcover. Condizione: new. Hardcover. This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field. This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.…

Condizione: New.

Hardcover. Condizione: Brand New. 192 pages. 9.25x6.10x0.51 inches. In Stock.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clusterin.…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field. 192 pp. Englisch.…

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 192 pp. Englisch.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.