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

estrange

書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning影響因子(影響力)




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning影響因子(影響力)學(xué)科排名




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning網(wǎng)絡(luò)公開度




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning被引頻次




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning被引頻次學(xué)科排名




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning年度引用




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning年度引用學(xué)科排名




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning讀者反饋




書目名稱Intelligence Science and Big Data Engineering. Big Data and Machine Learning讀者反饋學(xué)科排名

低三下四之人

978-3-030-36203-4Springer Nature Switzerland AG 2019

騷動

Intelligence Science and Big Data Engineering. Big Data and Machine Learning978-3-030-36204-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

MAG

https://doi.org/10.1007/978-3-030-36204-1artificial intelligence; computational linguistics; computer networks; computer vision; data mining; face

遺忘

HALL

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.

多產(chǎn)子

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.

notion

不可救藥

光滑

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