Intelligence Science and Big Data Engineering. Big Data and Machine Learning : 9th International Conference, IScIDE 2019, Nanjing, China, October 17¿20, 2019, Proceedings, Part II

Zhen Cui

ISBN 10: 3030362035 / ISBN 13: 9783030362034

Editore: Springer International Publishing, Springer International Publishing, 2019

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019.The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topicsincluding information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

Codice articolo 9783030362034

Dati bibliografici

• Titolo: Intelligence Science and Big Data Engineering. Big Data and Machine Learning : 9th International Conference, IScIDE 2019, Nanjing, China, October 17¿20, 2019, Proceedings, Part II
• Editore: Springer International Publishing, Springer International Publishing
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030362035
• ISBN 13: 9783030362034
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

Riassunto

The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019.

The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.