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unsupervised feature extraction applied di taguchi (30 risultati)
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![Immagine dell'editore per Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning) [Paperback] Taguchi, Y-h. venduto da SpringBooks](https://pictures.abebooks.com/isbn/9783030224585-it-300.jpg)
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![Immagine dell'editore per Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning) [Hardcover] Taguchi, Y-h. venduto da SpringBooks](https://pictures.abebooks.com/isbn/9783030224554-it-300.jpg)
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
Da: Books Puddle, New York, NY, U.S.A.
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EUR 180,13
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Unsupervised Feature Extraction Applied to Bioinformatics
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
Da: Majestic Books, Hounslow, Regno Unito
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
Da: Biblios, Frankfurt am main, HESSE, Germania
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Unsupervised Feature Extraction Applied to Bioinformatics
Da: Books Puddle, New York, NY, U.S.A.
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EUR 201,25
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Aggiungi al carrelloCondizione: New. pp. XVIII, 321 111 illus., 94 illus. in color. 1 Edition NO-PA16APR2015-KAP.
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Unsupervised Feature Extraction Applied to Bioinformatics : A Pca Based and Td Based Approach
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Unsupervised Feature Extraction Applied to Bioinformatics : A PCA Based and TD Based Approach
Editore: Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
EUR 213,99
Convertire valutaEUR 14,99 per la spedizione da Germania a ItaliaQuantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: 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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Unsupervised Feature Extraction Applied to Bioinformatics
Editore: Springer International Publishing, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
EUR 213,99
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Aggiungi al carrelloBuch. Condizione: 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 (Unsupervised and Semi-Supervised Learning)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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EUR 226,58
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
Da: California Books, Miami, FL, U.S.A.
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EUR 250,90
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Unsupervised Feature Extraction Applied to Bioinformatics : A Pca Based and Td Based Approach
Da: GreatBookPrices, Columbia, MD, U.S.A.
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EUR 242,07
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach
Da: Revaluation Books, Exeter, Regno Unito
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EUR 259,42
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Aggiungi al carrelloHardcover. Condizione: Brand New. 321 pages. 9.25x6.25x0.75 inches. In Stock.
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Unsupervised Feature Extraction Applied to Bioinformatics (Hardcover)
Editore: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 237,55
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Aggiungi al carrelloHardcover. Condizione: 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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EUR 287,04
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Aggiungi al carrelloCondizione: New. 2024. Second Edition 2024. hardcover. . . . . .
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Unsupervised Feature Extraction Applied to Bioinformatics (Hardcover)
Editore: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
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EUR 234,04
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Aggiungi al carrelloHardcover. Condizione: 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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Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and Td Based Approach
Da: Revaluation Books, Exeter, Regno Unito
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EUR 306,66
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Aggiungi al carrelloHardcover. Condizione: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock.
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Unsupervised Feature Extraction Applied to Bioinformatics (Hardcover)
Editore: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
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EUR 317,96
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Aggiungi al carrelloHardcover. Condizione: 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: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
Da: Kennys Bookstore, Olney, MD, U.S.A.
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EUR 360,29
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Aggiungi al carrelloCondizione: New. 2024. Second Edition 2024. hardcover. . . . . . Books ship from the US and Ireland.
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Unsupervised Feature Extraction Applied to Bioinformatics (eng)
Da: Brook Bookstore On Demand, Napoli, NA, Italia
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EUR 134,27
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Unsupervised Feature Extraction Applied to Bioinformatics
Editore: Springer, Springer Sep 2025, 2025
ISBN 10: 3031609840 ISBN 13: 9783031609848
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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EUR 171,19
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 556 pp. Englisch.
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Unsupervised Feature Extraction Applied to Bioinformatics
Editore: Springer, Springer Sep 2025, 2025
ISBN 10: 3031609840 ISBN 13: 9783031609848
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 556 pp. Englisch.
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Unsupervised Feature Extraction Applied to Bioinformatics
Editore: Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
EUR 180,07
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Aggiungi al carrelloGebunden. Condizione: 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
Da: Majestic Books, Hounslow, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 212,42
Convertire valutaEUR 10,18 per la spedizione da Regno Unito a ItaliaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand pp. XVIII, 321 111 illus., 94 illus. in color.
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Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and Td Based Approach
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 213,27
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Aggiungi al carrelloHardcover. Condizione: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.
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Unsupervised Feature Extraction Applied to Bioinformatics
Da: Biblios, Frankfurt am main, HESSE, Germania
Valutazione del venditore 5 su 5 stelle
EUR 215,14
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. XVIII, 321 111 illus., 94 illus. in color.
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Unsupervised Feature Extraction Applied to Bioinformatics
Editore: Springer, Berlin, Springer International Publishing, Springer, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
EUR 213,99
Convertire valutaEUR 11,00 per la spedizione da Germania a ItaliaQuantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: 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.
