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Feature Dimensionality Reduction Clustering by Ros Frederic (19 results)
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Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 103.28
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Condition: As New. Unread book in perfect condition.
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Feature and Dimensionality Reduction for Clustering With Deep Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 103.27
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Paperback. Condition: Brand New. 9.26x6.11 inches. In Stock.
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Feature and Dimensionality Reduction for Clustering with Deep Learning (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031487451 ISBN 13: 9783031487453
Paperback. Condition: new. Paperback. This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by family to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers. This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Feature and Dimensionality Reduction for Clustering With Deep Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 114.85
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Feature and Dimensionality Reduction for Clustering with Deep Learning
Language: English
Published by Springer Nature Switzerland, 2024
ISBN 10: 3031487427 ISBN 13: 9783031487422
Gebunden. Condition: New.
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Taschenbuch. Condition: Neu. Feature and Dimensionality Reduction for Clustering with Deep Learning | Frederic Ros (u. a.) | Taschenbuch | Unsupervised and Semi-Supervised Learning | xi | Englisch | 2025 | Springer | EAN 9783031487453 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Hardcover. Condition: Brand New. 279 pages. 9.25x6.10x0.91 inches. In Stock.
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Feature and Dimensionality Reduction for Clustering with Deep Learning (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031487451 ISBN 13: 9783031487453
Paperback. Condition: new. Paperback. This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by family to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers. This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Condition: new. Questo è un articolo print on demand.
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Feature and Dimensionality Reduction for Clustering with Deep Learning
Language: English
Published by Berlin Springer Nature Switzerland Springer, 2024
ISBN 10: 3031487427 ISBN 13: 9783031487422
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and 'tricks' participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by 'family' to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers. 268 pp. Englisch.
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Feature and Dimensionality Reduction for Clustering with Deep Learning
Language: English
Published by Springer Verlag GmbH, 2025
ISBN 10: 3031487451 ISBN 13: 9783031487453
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Condition: New. Print on Demand.
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Feature and Dimensionality Reduction for Clustering with Deep Learning
Language: English
Published by Springer, Springer Jan 2024, 2024
ISBN 10: 3031487427 ISBN 13: 9783031487422
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and 'tricks' participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by żfamilyż to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 280 pp. Englisch.
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Feature and Dimensionality Reduction for Clustering with Deep Learning
Language: English
Published by Springer, Springer Jan 2025, 2025
ISBN 10: 3031487451 ISBN 13: 9783031487453
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and 'tricks' participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by 'family' to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 280 pp. Englisch.
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Condition: New. PRINT ON DEMAND.
