Isbn: 9783031609848 - unsupervised feature extraction applied to bioinformatics: a pca based and td based approach (unsupervised and semi-supervised learning) (8 resultados)

Taschenbuch. Condición: Neu. Unsupervised Feature Extraction Applied to Bioinformatics | A PCA Based and TD Based Approach | Y-h. Taguchi | Taschenbuch | Unsupervised and Semi-Supervised Learning | 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.…

Taschenbuch. Condición: 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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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 556 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 556 pp. Englisch.…

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