Anbieter: SpringBooks, Berlin, Deutschland
Erstausgabe
Softcover. Zustand: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.
Anbieter: SpringBooks, Berlin, Deutschland
Erstausgabe
Hardcover. Zustand: Very Good. 1. Auflage. Unread, with some shelfwear. Immediately dispatched from Germany.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 186,50
Anzahl: 1 verfügbar
In den WarenkorbZustand: New.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: 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.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 257,42
Anzahl: 1 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 321 pages. 9.25x6.25x0.75 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: 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.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031609816 ISBN 13: 9783031609817
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: 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.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 302,87
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock.