Intelligent Data Engineering and Automated Learning - IDEAL 2018
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This two-volume set LNCS 11314 and 11315 constitutes the 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,…mehr

Produktbeschreibung
This two-volume set LNCS 11314 and 11315 constitutes the 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.
  • Produktdetails
  • Lecture Notes in Computer Science .11314
  • Verlag: Springer, Berlin; Springer International Publishing
  • Artikelnr. des Verlages: 978-3-030-03492-4
  • 1st ed. 2018
  • Erscheinungstermin: 9. November 2018
  • Englisch
  • Abmessung: 233mm x 157mm x 60mm
  • Gewicht: 1323g
  • ISBN-13: 9783030034924
  • ISBN-10: 3030034925
  • Artikelnr.: 54101320
Inhaltsangabe
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.