And the TF-IDF based and explicit feedback based models were also constructed for performance comparison.
为了对比，本文也引入了基于TF - IDF的和显示反馈的用户模型。
To make up for the original TF-IDF formula defects, an improved TF-IDF formula, which combines concentration of a feature among categories, distribution of a feature in category, is proposed.
进而结合了类间集中度、类内分散度，提出一种TF - IDF公式的改进形式，来弥补原始tf - IDF方法的缺陷。
To verify efficiency of the new feature selection approach and improved TF-IDF formula, a multi-set of experiments base on the Chinese text categorization test system platform have been taken.
本文在中文文本分类实验平台上，通过多组对比实验来考察本文提出的新的特征提取方法和改进的TF-IDF方法的有效性。
Text representation approaches with term weighting schemes such as commonly used TF/IDF are widely used to extract indexing terms of documents.
文本表达是指将表达文献主题内容的词汇抽取出来的过程。常用的向量空间表达法主要采用TF/IDF等权重法。
Text representation approaches with term weighting schemes such as commonly used TF/IDF are widely used to extract indexing terms of documents.
文本表达是指将表达文献主题内容的词汇抽取出来的过程。常用的向量空间表达法主要采用TF/IDF等权重法。
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