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Record Nr. |
UNISA996466243303316 |
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Titolo |
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. Tallón-Ballesteros |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (XVI, 647 p. 209 illus.) |
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Collana |
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Information Systems and Applications, incl. Internet/Web, and HCI ; ; 9937 |
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Disciplina |
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Soggetti |
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Data mining |
Pattern recognition |
Artificial intelligence |
Algorithms |
Information storage and retrieval |
Computers |
Data Mining and Knowledge Discovery |
Pattern Recognition |
Artificial Intelligence |
Algorithm Analysis and Problem Complexity |
Information Storage and Retrieval |
Computation by Abstract Devices |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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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Sommario/riassunto |
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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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