Intelligent Data Engineering and Automated Learning – IDEAL 2016

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

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Database Management, General Computing
Cover of the book Intelligent Data Engineering and Automated Learning – IDEAL 2016 by , Springer International Publishing
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Author: ISBN: 9783319462578
Publisher: Springer International Publishing Publication: September 12, 2016
Imprint: Springer Language: English
Author:
ISBN: 9783319462578
Publisher: Springer International Publishing
Publication: September 12, 2016
Imprint: Springer
Language: English

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.

 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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