So, it is necessary to study feature selection algorithms.
所以，对特征选择算法的研究是十分必要的。
The author gains insights from attribute reduction based on discernability matrix and proposes a few rough-set based text feature selection algorithms, i. e. , DB1, DB2 and LDB.
作者从基于分辨矩阵的粗糙集属性约简中受到启发，提出了一系列基于粗集理论的文本特征选择算法，即DB1、DB2、LDB。
Although there exist numerous feature selection algorithms, new challenging research issues arise for feature selection: from handling a large dimensionality huge number of samples.
尽管出现了大量的特征选择算法，特征选择仍然面临着新的挑战：如何处理高维海量的样本。
According to various of applications of the datasets, feature selection algorithms can be categorized as either supervised learning or unsupervised learning feature selection approaches.
属性选择问题可以分为有指导学习环境下的选择和无指导学习环境下的选择。
Margin-based algorithms which use the large margin principle for feature selection have already played a crucial role in current machine learning research.
利用最大余量原理的特征选择算法在目前的机器学习研究中已经占据了重要的地位。
This paper presents two algorithms of linear feature selection used for the study of strong earthquake zoning in Beijing area.
本文介绍两种线性特征选择算法，并用于北京地区强震危险区划的研究。
And it introduces some algorithms of decision tree learning such as ID3, C4.5 and feature subset selection of Inductive learning.
介绍了归纳学习中的决策树学习算法如id3、C4.5和特征子集选择问题。
Right View Selection menu access mode features, feature extraction, the choice of cluster analysis algorithms.
右视图选择菜单获得模式特征，进行特征提取后，选择各种算法进行聚类分析。
Right View Selection menu access mode features, feature extraction, the choice of cluster analysis algorithms.
右视图选择菜单获得模式特征，进行特征提取后，选择各种算法进行聚类分析。
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