Intelligent Data Engineering and Automated Learning – IDEAL 2017

18th International Conference, Guilin, China, October 30 – November 1, 2017, Proceedings

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Database Management, General Computing
Cover of the book Intelligent Data Engineering and Automated Learning – IDEAL 2017 by , Springer International Publishing
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Author: ISBN: 9783319689357
Publisher: Springer International Publishing Publication: October 23, 2017
Imprint: Springer Language: English
Author:
ISBN: 9783319689357
Publisher: Springer International Publishing
Publication: October 23, 2017
Imprint: Springer
Language: English

This book constitutes the refereed proceedings of the 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017, held in Guilin, China, in October/November 2017.

The 65 full papers presented were carefully reviewed and selected from 110 submissions. These papers provided a sample of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media 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 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017, held in Guilin, China, in October/November 2017.

The 65 full papers presented were carefully reviewed and selected from 110 submissions. These papers provided a sample of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.

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