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Record Nr. |
UNISA996466314103316 |
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Titolo |
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XXVI, 865 p. 285 illus., 197 illus. in color.) |
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Collana |
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Information Systems and Applications, incl. Internet/Web, and HCI ; ; 11314 |
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Disciplina |
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Soggetti |
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Data mining |
Artificial intelligence |
Optical data processing |
Computers |
Data Mining and Knowledge Discovery |
Artificial Intelligence |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Theory of Computation |
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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 contenuto |
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Intelligent data analysis -- data mining and their associated learning systems and paradigms -- big data challenges -- machine learning, data mining, information retrieval and management -- bio- and neuro-informatics -- bio-inspired models including neural networks, evolutionary computation and swarm intelligence -- agents and hybrid intelligent systems, and real-world applications of intelligent techniques -- evolutionary algorithms -- deep learning neural networks -- probabilistic modeling -- 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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Sommario/riassunto |
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This two-volume set LNCS 11314 and 11315 constitutes the |
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thoroughly refereed conference proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, held in Madrid, Spain, in November 2018. The 125 full papers presented were carefully reviewed and selected from 204 submissions. These papers provided a timely sample of the latest advances 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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