一种选择特征的稀疏在线学习算法
发布时间：2019-07-10 16:54
【摘要】：为了有效处理海量、高维、稀疏的大数据,提高对数据的分类效率,提出一种基于L_1准则稀疏性原理的在线学习算法(a sparse online learning algorithm for selection feature,SFSOL)。运用在线机器学习算法框架,对高维流式数据的特征进行新颖的"取整"处理,加大数据特征稀疏性的同时增强了阀值范围内部分特征的值,极大地提高了对稀疏数据分类的效果。利用公开的数据集对SFSOL算法的性能进行分析,并将该算法与其它3种稀疏在线学习算法的性能进行比较,试验结果表明提出的SFSOL算法对高维稀疏数据分类的准确性更高。
[Abstract]:In order to deal with massive, high-dimensional and sparse big data effectively and improve the classification efficiency of data, an online learning algorithm (a sparse online learning algorithm for selection feature,SFSOL based on the sparse principle of L 鈮，
本文编号：2512755
[Abstract]:In order to deal with massive, high-dimensional and sparse big data effectively and improve the classification efficiency of data, an online learning algorithm (a sparse online learning algorithm for selection feature,SFSOL based on the sparse principle of L 鈮，
本文编号：2512755
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