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Titlebook: Intelligence Science and Big Data Engineering. Big Data and Machine Learning; 9th International Co Zhen Cui,Jinshan Pan,Jian Yang Conferenc

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发表于 2025-3-21 20:06:07 | 显示全部楼层 |阅读模式
书目名称Intelligence Science and Big Data Engineering. Big Data and Machine Learning
副标题9th International Co
编辑Zhen Cui,Jinshan Pan,Jian Yang
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligence Science and Big Data Engineering. Big Data and Machine Learning; 9th International Co Zhen Cui,Jinshan Pan,Jian Yang Conferenc
描述.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... .
出版日期Conference proceedings 2019
关键词artificial intelligence; computational linguistics; computer networks; computer vision; data mining; face
版次1
doihttps://doi.org/10.1007/978-3-030-36204-1
isbn_softcover978-3-030-36203-4
isbn_ebook978-3-030-36204-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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发表于 2025-3-21 20:29:25 | 显示全部楼层
978-3-030-36203-4Springer Nature Switzerland AG 2019
发表于 2025-3-22 02:18:11 | 显示全部楼层
Intelligence Science and Big Data Engineering. Big Data and Machine Learning978-3-030-36204-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
发表于 2025-3-22 07:59:55 | 显示全部楼层
https://doi.org/10.1007/978-3-030-36204-1artificial intelligence; computational linguistics; computer networks; computer vision; data mining; face
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Computational Decomposition of Style for Controllable and Enhanced Style Transfer,thod, we derive a simple, effective computational module, which can be embedded into state-of-the-art style transfer algorithms. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also other possible applications such as picture-to-sketch problems.
发表于 2025-3-22 20:04:23 | 显示全部楼层
Laplacian Welsch Regularization for Robust Semi-supervised Dictionary Learning,atic (HQ) optimization algorithm to solve the model efficiently. Experimental results on various real-world datasets show that LWR performs robustly to outliers and achieves the top-level results when compared with the existing algorithms.
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Mining Meta-association Rules for Different Types of Traffic Accidents,eta-rule set with universal applicability. Eventually, all traffic databases are excavated again with different thresholds to get association rules, and meta-rules are integrated into association rules to obtain the universal association rules in the form of a cell group. The proposed method is test
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