Description du livre Buch. Etat : 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. N� de r�f. du vendeur 9783030011796

Plus d'informations sur ce vendeur | Contacter le vendeur

Acheter neuf

EUR 160,49

Autre devise

Ajouter au panier

Frais de port : EUR 23

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Image fournie par le vendeur

Deep Learning and Missing Data in Engineering Systems

Collins Achepsah Leke|Tshilidzi Marwala

Edit� par Springer International Publishing (2019)

ISBN 10 : 3030011798 ISBN 13 : 9783030011796

Neuf Couverture rigide Quantit� disponible : > 20

impression � la demande

Vendeur :

moluna
(Greven, Allemagne)

Evaluation vendeur

Evaluation du vendeur :

Description du livre Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Adopts and applies&nbspswarm intelligence algorithms&nbspto address critical questions such as model selection and model parameter estimationProposes new paradigms of machine learning and computational intelligence in missing data esti. N� de r�f. du vendeur 241310952

Plus d'informations sur ce vendeur | Contacter le vendeur

Acheter neuf

EUR 136,16

Autre devise

Ajouter au panier

Frais de port : EUR 48,99

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Image fournie par le vendeur

Deep Learning and Missing Data in Engineering Systems

Tshilidzi Marwala

Edit� par Springer International Publishing (2019)

ISBN 10 : 3030011798 ISBN 13 : 9783030011796

Neuf Couverture rigide Quantit� disponible : 1

Vendeur :

AHA-BUCH GmbH
(Einbeck, Allemagne)

Evaluation vendeur

Evaluation du vendeur :

Description du livre Buch. Etat : 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. N� de r�f. du vendeur 9783030011796

Plus d'informations sur ce vendeur | Contacter le vendeur

Acheter neuf

EUR 164,03

Autre devise

Ajouter au panier

Frais de port : EUR 32,99

De Allemagne vers Etats-Unis

Destinations, frais et d�lais

Image d'archives