Robust Representation for Data Analytics: Models and Applications - Couverture souple

Livre 54 sur 66: Advanced Information and Knowledge Processing

Li, Sheng

9783319867960: Robust Representation for Data Analytics: Models and Applications

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ISBN 10 : 3319867962 ISBN 13 : 9783319867960

Editeur : Springer, 2019

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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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�diteur: Springer
Date d'�dition: 2019
Langue: anglais
ISBN 10: 3319867962
ISBN 13: 9783319867960
Reliure: Broch�
Nombre de pages: 236
Coordonn�es du fabricant: ProductSafety@springernature.com
ProductSafety@springernature.com

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Darmstadt
64293
Allemagne

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Li, Sheng

Edit� par Springer, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

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Robust Representation for Data Analytics

Yun Fu

Edit� par Springer International Publishing Aug 2018, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

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Taschenbuch. 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 r�f. du vendeur 9783319867960

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Robust Representation for Data Analytics

Sheng Li|Yun Fu

Edit� par Springer International Publishing, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

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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 r�f. du vendeur 458627088

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Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

Li, Sheng

Edit� par Springer, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

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Robust Representation for Data Analytics | Models and Applications

Sheng Li (u. a.)

Edit� par Springer, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

Neuf Taschenbuch

Vendeur : preigu, Osnabr�ck, Allemagne

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Robust Representation for Data Analytics

Sheng Li

Edit� par Springer, Palgrave Macmillan Aug 2018, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. 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 r�f. du vendeur 9783319867960

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Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

Li, Sheng

Edit� par Springer, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

Neuf Couverture souple

impression � la demande

Vendeur : Majestic Books, Hounslow, Royaume-Uni

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Etat : New. Print on Demand. N� de r�f. du vendeur 369607156

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Robust Representation for Data Analytics : Models and Applications

Yun Fu

Edit� par Springer International Publishing, 2018

ISBN 10 : 3319867962 ISBN 13 : 9783319867960

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. 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 r�f. du vendeur 9783319867960

Contacter le vendeur