Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods - Couverture souple

Livre 31 sur 86: Advances in Computer Vision and Pattern Recognition

Aldrich, Chris

9781447171607: Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

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ISBN 10 : 1447171608 ISBN 13 : 9781447171607

Editeur : Springer, 2016

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This book describes the latest developments in nonlinear methods and their application in fault diagnosis. It details advances in machine learning theory and contains numerous case studies with real-world data from industry.

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�diteur: Springer
Date d'�dition: 2016
Langue: anglais
ISBN 10: 1447171608
ISBN 13: 9781447171607
Reliure: Broch�
Nombre de pages: 396
Coordonn�es du fabricant: non disponible
Personne responsable: non disponible

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (eng)

Aldrich, Chris

Edit� par Springer, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Lidia Auret

Edit� par Springer London Aug 2016, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis. 396 pp. Englisch. N� de r�f. du vendeur 9781447171607

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Chris Aldrich|Lidia Auret

Edit� par Springer London, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Describes the latest developments in nonlinear methods and their application in fault diagnosisDiscusses in detail several advances in machine learning theoryContains numerous case studies with real-world data from industryThis uniq. N� de r�f. du vendeur 458490319

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Lidia Auret (u. a.)

Edit� par Springer London, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Lidia Auret

Edit� par Springer London, Springer London Aug 2016, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 396 pp. Englisch. N� de r�f. du vendeur 9781447171607

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Lidia Auret

Edit� par Springer London, Springer London, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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 unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis. N� de r�f. du vendeur 9781447171607

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)

Chris Aldrich

Edit� par Springer, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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Vendeur : Revaluation Books, Exeter, Royaume-Uni

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Paperback. Etat : Brand New. reprint edition. 393 pages. 9.25x6.10x0.93 inches. In Stock. N� de r�f. du vendeur zk1447171608

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)

Aldrich, Chris, Auret, Lidia

Edit� par Springer, 2016

ISBN 10 : 1447171608 ISBN 13 : 9781447171607

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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni

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Paperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book. N� de r�f. du vendeur ERICA80014471716086

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