A kind of improved weighting TFIDF algorithm is proposed based on the page relevant weight and the TFIDF algorithm.
将页面相关性权重与TFIDF算法相结合，提出了一种加权TFIDF算法。
Make use of the technology of information filtering based on TFIDF to filter the results returned by the searching engine.
采用了基于加权向量空间法的信息过滤技术，对成员搜索引擎返回的结果进行过滤。
Moreover, S-TFIDF algorithm is as efficient as TFIDF algorithm, which implies it is competent for large scale text categorization task.
同时，S -TFIDF算法保持了TFIDF算法的高运行效率，适合大规模的文本分类任务。
Because the HTML page contains rich information, the traditional TFIDF formula is difficult to meet the requirements of content filtering systems.
网页包含的信息很丰富，传统的TFIDF公式很难满足内容过滤系统的要求。
Iterative TFIDF algorithm belongs to hill-climbing algorithm, it has the common problem of converging to local optimal value and sensitive to initial point.
迭代tfidf算法属于爬山算法，初始值的选取对精度影响较大，算法容易收敛到局部最优值。
Some feature extraction methods for web filtering exist problems, semantic information is added, the TFIDF formula is improved and then a method of feature extraction is proposed.
针对网页过滤技术中的特征选择方法存在的问题，加入语义信息，改进TFIDF公式，提出了一种比较适合网页过滤的特征选择方法。
Some feature extraction methods for web filtering exist problems, semantic information is added, the TFIDF formula is improved and then a method of feature extraction is proposed.
针对网页过滤技术中的特征选择方法存在的问题，加入语义信息，改进TFIDF公式，提出了一种比较适合网页过滤的特征选择方法。
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