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Graph Embedding Pattern Analysis (23 results)
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Graph Embedding for Pattern Analysis
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 134.44
US$ 16.26 shipping
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Graph Embedding for Pattern Analysis
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 134.44
US$ 16.26 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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US$ 132.59
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Add to basketPaperback. Condition: New.
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Condition: New. pp. 270.
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Condition: New. pp. 268.
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US$ 176.36
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Add to basketPaperback. Condition: Brand New. 268 pages. 9.30x6.20x0.63 inches. In Stock.
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Add to basketHardcover. Condition: Brand New. 2013 edition. 268 pages. 9.20x6.10x0.80 inches. In Stock.
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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.
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Graph Embedding for Pattern Analysis
Language: English
Published by Springer New York, Springer New York, 2012
ISBN 10: 146144456X ISBN 13: 9781461444565
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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Taschenbuch. Condition: Neu. Druck auf Anfrage 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.
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Graph Embedding for Pattern Analysis
Language: English
Published by Springer New York Nov 2012, 2012
ISBN 10: 146144456X ISBN 13: 9781461444565
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 -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.
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Graph Embedding for Pattern Analysis
Language: English
Published by Springer New York Dez 2014, 2014
ISBN 10: 1489990623 ISBN 13: 9781489990624
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 -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.
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US$ 157.49
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Ships from United Kingdom to U.S.A.Quantity: 4 available
Add to basketCondition: New. Print on Demand pp. 270 91 Illus. (63 Col.).
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US$ 111.39
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Ships from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: 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.
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US$ 111.39
US$ 57.42 shipping
Ships from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: 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.
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US$ 158.84
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Ships from United Kingdom to U.S.A.Quantity: 4 available
Add to basketCondition: New. Print on Demand pp. 268.
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Condition: New. PRINT ON DEMAND pp. 268.
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Condition: New. PRINT ON DEMAND pp. 270.
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Buch. Condition: Neu. Graph Embedding for Pattern Analysis | Yun Fu (u. a.) | Buch | viii | Englisch | 2012 | Springer | EAN 9781461444565 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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Graph Embedding for Pattern Analysis
Language: English
Published by Springer, Springer Dez 2014, 2014
ISBN 10: 1489990623 ISBN 13: 9781489990624
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 -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.
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Graph Embedding for Pattern Analysis
Language: English
Published by Springer, Springer Nov 2012, 2012
ISBN 10: 146144456X ISBN 13: 9781461444565
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
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.
