Using latent semantic analysis to extract feature, the affect of synonymy and polysemy in text representation process is eliminated and the dimension of text vector is reduced.
利用潜在语义分析进行特征抽取，消除多义词和同义词在文本表示时造成的偏差，并实现文本向量的降维。
Feature selection is a valid method to reduce the dimension of text vector in automatic text categorization system.
在自动文本分类系统中，特征选择是有效降低文本向量维数的一种方法。
Based on RGB color space, this text gives the method of feature extraction and the method of compute the feature vector.
本文给出了基于RGB颜色空间的特征提取方法和特征向量的色差计算方法。
To convert the text into digital form, we apply a feature-based augmented vector model on text of the directory.
通过对目录的结构和特征进行分析，本文提出了一种基于特征的目录结构表示模型。
To convert the text into digital form, we apply a feature-based augmented vector model on text of the directory.
通过对目录的结构和特征进行分析，本文提出了一种基于特征的目录结构表示模型。
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