Graph Embedding Pattern Analysis (23 results)

Condition: New. In.

Condition: New. In.

Paperback. Condition: New.

Condition: New. pp. 268.

Condition: New. pp. 270.

Paperback. Condition: Brand New. 268 pages. 9.30x6.20x0.63 inches. In Stock.

Hardcover. Condition: Brand New. 2013 edition. 268 pages. 9.20x6.10x0.80 inches. In Stock.

Taschenbuch. Condition: Neu. Graph Embedding for Pattern Analysis | Yun Fu (u. a.) | Taschenbuch | viii | Englisch | 2014 | Springer | EAN 9781489990624 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.…

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Condition: new. Questo è un articolo print on demand.

Condition: new. Questo è un articolo print on demand.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field. 268 pp. Englisch.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field. 268 pp. Englisch.…

Condition: New. Print on Demand pp. 268.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers theoretical analysis and real-world applications for graph embedding Examines subspace analysis with L1 graph Describes graph-based inference on Riemannian manifolds for visual analysisDr. Yun Fu is a professor at the State U.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers theoretical analysis and real-world applications for graph embedding Examines subspace analysis with L1 graph Describes graph-based inference on Riemannian manifolds for visual analysisDr. Yun Fu is a professor at the State U.…

Condition: New. Print on Demand pp. 270 91 Illus. (63 Col.).

Condition: New. PRINT ON DEMAND pp. 268.

Condition: New. PRINT ON DEMAND pp. 270.

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 268 pp. Englisch.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 268 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.…