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161005 ||| eng |
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|a 9783319462578
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100 |
1 |
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|a Yin, Hujun
|e [editor]
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245 |
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|a Intelligent Data Engineering and Automated Learning – IDEAL 2016
|h Elektronische Ressource
|b 17th International Conference, Yangzhou, China, October 12–14, 2016, Proceedings
|c edited by Hujun Yin, Yang Gao, Bin Li, Daoqiang Zhang, Ming Yang, Yun Li, Frank Klawonn, Antonio J. Tallón-Ballesteros
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250 |
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|a 1st ed. 2016
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260 |
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|a Cham
|b Springer International Publishing
|c 2016, 2016
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300 |
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|a XVI, 647 p. 209 illus
|b online resource
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505 |
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|a 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
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653 |
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|a Computer science
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653 |
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|a Information Storage and Retrieval
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653 |
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|a Artificial Intelligence
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653 |
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|a Algorithms
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653 |
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|a Data mining
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653 |
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|a Information storage and retrieval systems
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653 |
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|a Artificial intelligence
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653 |
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|a Data Mining and Knowledge Discovery
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653 |
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|a Theory of Computation
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653 |
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|a Automated Pattern Recognition
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653 |
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|a Pattern recognition systems
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700 |
1 |
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|a Gao, Yang
|e [editor]
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700 |
1 |
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|a Li, Bin
|e [editor]
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700 |
1 |
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|a Zhang, Daoqiang
|e [editor]
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
989 |
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|b Springer
|a Springer eBooks 2005-
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490 |
0 |
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|a Information Systems and Applications, incl. Internet/Web, and HCI
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028 |
5 |
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|a 10.1007/978-3-319-46257-8
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856 |
4 |
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|u https://doi.org/10.1007/978-3-319-46257-8?nosfx=y
|x Verlag
|3 Volltext
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|a 006.312
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520 |
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|a 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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