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Unsupervised Feature Extraction Applied by Taguchi Y H (24 results)
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![Stock image for Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning) [Paperback] Taguchi, Y-h. for sale by SpringBooks](https://pictures.abebooks.com/isbn/9783030224585-us-300.jpg)
Softcover. Condition: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.
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![Stock image for Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning) [Hardcover] Taguchi, Y-h. for sale by SpringBooks](https://pictures.abebooks.com/isbn/9783030224554-us-300.jpg)
Hardcover. Condition: Very Good. 1. Auflage. Unread, with some shelfwear. Immediately dispatched from Germany.
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Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
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Condition: New. Second Edition 2024 NO-PA16APR2015-KAP.
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US$ 213.22
US$ 8.75 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketCondition: New.
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Condition: New. pp. XVIII, 321 111 illus., 94 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Unsupervised Feature Extraction Applied to Bioinformatics (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Hardcover. Condition: new. Hardcover. This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Taschenbuch. Condition: Neu. Unsupervised Feature Extraction Applied to Bioinformatics | A PCA Based and TD Based Approach | Y-h. Taguchi | Taschenbuch | xxii | Englisch | 2025 | Springer | EAN 9783031609848 | 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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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering.
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US$ 303.74
US$ 16.84 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: Brand New. 321 pages. 9.25x6.25x0.75 inches. In Stock.
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Unsupervised Feature Extraction Applied to Bioinformatics
Language: English
Published by Springer International Publishing, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. Neuware -This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 556 pp. Englisch.
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Unsupervised Feature Extraction Applied to Bioinformatics : A PCA Based and TD Based Approach
Language: English
Published by Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
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US$ 355.80
US$ 16.84 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketHardcover. Condition: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock.
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Unsupervised Feature Extraction Applied to Bioinformatics (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Hardcover. Condition: new. Hardcover. This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Unsupervised Feature Extraction Applied to Bioinformatics
Language: English
Published by Springer, Springer Sep 2025, 2025
ISBN 10: 3031609840 ISBN 13: 9783031609848
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 556 pp. Englisch.
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US$ 238.37
US$ 8.75 shipping
Ships from United Kingdom to U.S.A.Quantity: 4 available
Add to basketCondition: New. Print on Demand pp. XVIII, 321 111 illus., 94 illus. in color.
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US$ 249.70
US$ 16.84 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.
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Condition: New. PRINT ON DEMAND pp. XVIII, 321 111 illus., 94 illus. in color.
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Unsupervised Feature Extraction Applied to Bioinformatics
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
US$ 218.21
US$ 57.64 shipping
Ships from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own .
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Unsupervised Feature Extraction Applied to Bioinformatics
Language: English
Published by Springer, Springer Sep 2025, 2025
ISBN 10: 3031609840 ISBN 13: 9783031609848
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 Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 556 pp. Englisch.
-
Unsupervised Feature Extraction Applied to Bioinformatics
Language: English
Published by Springer, Berlin, Springer International Publishing, Springer, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
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 updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. 527 pp. Englisch.
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Buch. Condition: Neu. Unsupervised Feature Extraction Applied to Bioinformatics | A PCA Based and TD Based Approach | Y-h. Taguchi | Buch | xxii | Englisch | 2024 | Springer | EAN 9783031609817 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
