基于隐马尔可夫模型的文本分类算法 - docin.com豆丁网 M 的文本分 类模型分类器训练要经过如下步骤。 1）特征选择及降维。特征选择有多种方法，如词频—反 文档频率（Term Frequency-Inverse Document Frequency，TF- IDF），信 息 增 益 （Information Gain，IG ），互 信 息 （Mutual Info
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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。
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。
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。
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