Intelligent Data Engineering and Automated Learning - IDEAL 2016 (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
647
Utgivningsdatum
2016-09-13
Upplaga
1st ed. 2016
Förlag
Springer International Publishing AG
Medarbetare
Yin, Hujun (ed.), Gao, Yang (ed.), Li, Bin (ed.), Zhang, Daoqiang (ed.), Yang, Ming (ed.), Li, Yun (ed.), Klawonn, Frank (ed.), Tallón-Ballesteros, Antonio J. (ed.)
Illustratör/Fotograf
Bibliographie 209 schwarz-weiße Abbildungen
Illustrationer
209 Illustrations, black and white; XVI, 647 p. 209 illus.
Dimensioner
234 x 156 x 34 mm
Vikt
917 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783319462561

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Intelligent Data Engineering and Automated Learning - IDEAL 2016 (häftad)

Intelligent Data Engineering and Automated Learning - IDEAL 2016

17th International Conference, Yangzhou, China, October 12-14, 2016, Proceedings

av Hujun Yin, Yang Gao, Bin Li, Daoqiang Zhang, Ming Yang
Häftad Engelska, 2016-09-13
1083
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This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, held in Yangzhou, China, in October 2016. The 68 full papers presented were carefully reviewed and selected from 115 submissions. They provide a valuable and timely sample of latest research outcomes in data engineering and automated learning ranging from methodologies, frameworks, and techniques to applications including various topics such as evolutionary algorithms; deep learning; neural networks; probabilistic modeling; particle swarm intelligence; big data analysis; applications in regression, classification, clustering, medical and biological modeling and predication; text processing and image analysis.
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Fler böcker av författarna

Hujun Yin, Yang Gao, Bin Li, Daoqiang Zhang, Ming Yang

Innehållsförteckning

Research outcomes in data engineering and automated learning.- Methodologies, frameworks, and techniques.- Applications including various topics such as evolutionary algorithms; deep learning; neural networks; probabilistic modeling; particle swarm intelligence; big data analysis.- Applications in regression, classification, clustering, medical and biological modeling and predication.- Text processing and image analysis.