Intelligent Data Engineering and Automated Learning - IDEAL 2016 [electronic resource] : 17th International Conference, Yangzhou, China, October 12-14, 2016, Proceedings / edited by Hujun Yin, Yang Gao, Bin Li, Daoqiang Zhang, Ming Yang, Yun Li, Frank Klawonn, Antonio J. Tall�on-Ballesteros.
Contributor(s): Yin, Hujun [editor.] | Gao, Yang [editor.] | Li, Bin [editor.] | Zhang, Daoqiang [editor.] | Yang, Ming [editor.] | Li, Yun [editor.] | Klawonn, Frank [editor.] | Tall�on-Ballesteros, Antonio J [editor.] | SpringerLink (Online service).
Material type: BookSeries: Lecture Notes in Computer Science: 9937Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XVI, 647 p. 209 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319462578.Subject(s): Computer science | Computers | Algorithms | Data mining | Information storage and retrieval | Artificial intelligence | Pattern recognition | Computer Science | Data Mining and Knowledge Discovery | Pattern Recognition | Artificial Intelligence (incl. Robotics) | Algorithm Analysis and Problem Complexity | Information Storage and Retrieval | Computation by Abstract DevicesAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access onlineResearch 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.
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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