This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.
Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning.Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
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Vendeur : SpringBooks, Berlin, Allemagne
Hardcover. Etat : Very Good. 1. Auflage. unread, with a mimimum of shelfwear. N° de réf. du vendeur CEN-2302C-CENKISTE1-38-1000XS
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Vendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning.Robust Representations for Data Analyticscovers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision. 236 pp. Englisch. N° de réf. du vendeur 9783319601755
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Vendeur : moluna, Greven, Allemagne
Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Enriches understanding of robust feature representationsExplains how to develop robust data mining modelsReinforces robust representation principles with real-world practiceThis book introduces the concepts and models of robust repr. N° de réf. du vendeur 150706205
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Vendeur : Books Puddle, New York, NY, Etats-Unis
Etat : New. pp. 220. N° de réf. du vendeur 26375300541
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Vendeur : preigu, Osnabrück, Allemagne
Buch. Etat : Neu. Robust Representation for Data Analytics | Models and Applications | Sheng Li (u. a.) | Buch | Advanced Information and Knowledge Processing | xi | Englisch | 2017 | Springer | EAN 9783319601755 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 109687890
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 236 pp. Englisch. N° de réf. du vendeur 9783319601755
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning.Robust Representations for Data Analyticscovers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision. N° de réf. du vendeur 9783319601755
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand pp. 220. N° de réf. du vendeur 371793506
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