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Intelligence Science and Big Data Engineering. Big Data and Machine Learning - 9th International Conference, IScIDE 2019,... 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 (Paperback, 1st ed. 2019)
Zhen Cui, Jinshan Pan, Shanshan Zhang, Liang Xiao, Jian Yang
R1,614 Discovery Miles 16 140 Ships in 10 - 15 working days

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.

Intelligence Science and Big Data Engineering. Visual Data Engineering - 9th International Conference, IScIDE 2019, Nanjing,... Intelligence Science and Big Data Engineering. Visual Data Engineering - 9th International Conference, IScIDE 2019, Nanjing, China, October 17-20, 2019, Proceedings, Part I (Paperback, 1st ed. 2019)
Zhen Cui, Jinshan Pan, Shanshan Zhang, Liang Xiao, Jian Yang
R2,544 Discovery Miles 25 440 Ships in 10 - 15 working days

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.

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