Articoli correlati a Deep Learning and Missing Data in Engineering Systems:...
Deep Learning and Missing Data in Engineering Systems: Applications to Engineering Systems: 48 - Rilegato
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2019
- ISBN 10 3030011798
- ISBN 13 9783030011796
- RilegaturaCopertina rigida
- Numero edizione1
- Numero di pagine196
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Deep Learning and Missing Data in Engineering Systems.
Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germania
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25 cm. XIV, 179 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Big Data. Volume 48. Sprache: Englisch. Codice articolo 6773FB
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Deep Learning And Missing Data In Engineering Systems
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. Codice articolo 26376460888
Quantità: 1 disponibili
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Deep Learning And Missing Data In Engineering Systems
Da: Majestic Books, Hounslow, Regno Unito
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Condizione: New. Codice articolo 369617287
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Deep Learning and Missing Data in Engineering Systems
Editore:
Springer International Publishing Jan 2019, 2019
ISBN 10: 3030011798
ISBN 13: 9783030011796
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including:deep autoencoder neural networks;deep denoising autoencoder networks;the bat algorithm;the cuckoo search algorithm; andthe firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation. 196 pp. Englisch. Codice articolo 9783030011796
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Deep Learning and Missing Data in Engineering Systems
Editore:
Springer International Publishing, 2019
ISBN 10: 3030011798
ISBN 13: 9783030011796
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Da: moluna, Greven, Germania
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Adopts and applies swarm intelligence algorithms to address critical questions such as model selection and model parameter estimationProposes new paradigms of machine learning and computational intelligence in missing data esti. Codice articolo 241310952
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Deep Learning and Missing Data in Engineering Systems
Editore:
Springer International Publishing, 2019
ISBN 10: 3030011798
ISBN 13: 9783030011796
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Rilegato
Da: AHA-BUCH GmbH, Einbeck, Germania
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including:deep autoencoder neural networks;deep denoising autoencoder networks;the bat algorithm;the cuckoo search algorithm; andthe firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation. Codice articolo 9783030011796
Quantità: 1 disponibili
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Deep Learning and Missing Data in Engineering Systems (Studies in Big Data)
Da: Mispah books, Redhill, SURRE, Regno Unito
Valutazione del venditore 4 su 5 stelle
Hardcover. Condizione: New. New. book. Codice articolo ERICA77330300117986
Quantità: 1 disponibili