Large-scale online feature selection for ultra-high dimensional sparse data

Large-scale online feature selection for ultra-high dimensional sparse data

Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may...

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書目詳細資料
Main Authors:	WU, Yue, HOI, Steven C. H., MEI, Tao, YU, Nenghai
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2017
主題:	Feature selection Second-order online learning Sparsity Ultra-high dimensionality Databases and Information Systems Numerical Analysis and Computation Theory and Algorithms
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/3781 https://ink.library.smu.edu.sg/context/sis_research/article/4783/viewcontent/Large_Scale_Online_Feature_Selection_Ultra_high_2017_afv.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

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