KNN algorithm is a common and effective text categorization algorithm.
KNN算法是一种常用的效果较好的文本分类算法。
The performance of text categorization algorithm based on centroid is poor when the documents are dispersive or existing more than one peak value.
当文本集较分散或出现多峰值时，基于质心的文本分类算法分类效果很差。
Aiming at this problem, this paper proposes an improved text categorization algorithm whose performance is higher than classical categorization algorithm based on centroid.
针对该问题提出一种改进的文本分类算法，与基于质心的经典分类算法相比，其性能较高。
In order to improve the performance of chemistry-focused search engines, an automatic text categorization algorithm is proposed based on the distance-weighted k-nearest neighbor algorithm.
为了提高化学主题搜索引擎的查询效果，采用距离加权七一近邻分类算法来进行自动分类。
Thus, the general categorization of this algorithm is best-first with coalescing: Freed chunks are coalesced with neighboring ones, and held in bins that are searched in size order.
因此，这个算法一般被归类为带合并的最佳适合算法：释放的邻近的块被合并，并被保存在按大小搜索的箱子里。
The main deducing steps are presented in the core-training algorithm of text categorization.
对于文本分类的核心训练算法，给出主要步骤的推导过程；
A reduction algorithm based on rough set is improved and then applicated to extract the rules of text categorization.
改进了一种粗糙集决策表的值约简算法，并将其应用到文本分类规则的提取中。
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。
In this paper, a flexible KNN algorithm is developed with varying-K algorithm and weighting algorithm, which improves the effect of text categorization.
基于近邻序列的排序，提出了变k算法，并且结合效果较好权重算法，形成了柔性的KNN算法，提高了分类的效果。
Experimental results show that the semantic categorization knowledge is useful for improving the learning efficiency of the algorithm and accuracy of disambiguation.
实验结果表明语义范畴的引入有助于提高算法的学习效率和词义排歧的正确率。
The experiment result has proved that the method can improve the class's categorization effect with fewer training samples of KNN algorithm.
实验结果表明此方法有效改善了KNN算法对少数类分类效果。
The experimental results show that the performance of text categorization model based on entropy is a relatively stable algorithm, and prove the effectiveness of the algorithm.
实验结果表明基于信息熵的文本分类模型是一种比较稳定的算法，证明了算法的有效性。
On the basis of immune algorithm, the authors propose a new method of text categorization called clonal selection algorithm based on antibody density.
借鉴了免疫系统的分类本质以及免疫系统的克隆选择和抗体浓度控制原理，提出了基于抗体浓度的克隆选择算法。
No matter what algorithm is selected, it can make up insufficient of current categorization deficient semantic relation to some extent. Enhance the document classification accuracy.
无论采取哪种算法，都可以在一定程度上弥补当前分类系统缺乏语义联系的不足，提高文本分类的准确性。
This paper presents a new algorithm using title category semantic recognition for text categorization.
本文提出了一种基于标题类别语义识别的文本分类算法。
This paper presents a new algorithm using title category semantic recognition for text categorization.
本文提出了一种基于标题类别语义识别的文本分类算法。
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