This paper presents a fast text classification algorithm based on KNN (K Nearest Neighbor).
提出了一种基于K近邻(KNN)原理的快速文本分类算法。
It plays an instructional role in academic study and practical application of KNN text classification algorithm.
对KNN文本分类算法的理论研究和实际应用起了指导作用。
Two important research directions of text classification are: feature selection method and text classification algorithm.
文本分类的两个重要的研究方向是：特征选择与文本分类算法。
Based on the characteristic that SMS can change into text, this paper presents a thought that text classification algorithm can be used in the technology of SMS processing.
根据短信可转化为文本的特性，将文本分类算法运用到短信处理技术之中。
This article aims to improve the algorithm of term weighting in automated text classification.
文章研究并改进了文本自动分类中的特征权重算法。
The results of experiment show that the improved algorithm advances the precision of text classification, and reduces the requirement of training scale.
试验结果证明此改进算法能够提高文本分类精度，很好的降低了分类器对训练规模的要求。
For text classification based on SVM learning algorithm, usually there is an abundance of training data, which will cost a lot of computing resources in training process.
在采用SVM算法的文本分类中，由于文本所表征的向量空间维数通常非常巨大，因此在训练过程中将耗费大量的系统资源。
Compare to other learning algorithm, SVM learning algorithm performances better in text classification.
相对于其他学习算法，SVM在文本的分类中表现出了更优异的性能。
Using rough set of the final value reduction algorithm for text classification rules extraction, thus gained the final text classification rules.
然后采用粗糙集的值约简算法来进行文本分类规则的抽取，从而得到最终的文本分类规则。
This text introduced the concept of the classification counter of attribute value, then introduced ID4 algorithm and ID5R algorithm especially, and compare at the end.
该文引入了属性值类别计数器的概念，然后重点介绍了ID4算法和ID5R算法，并在最后加以比较。
This paper presents a novel algorithm, which takes advantage of both hyper-link and text in the WEB pages, to mine WEB community by SVM classification.
论文结合WEB页的内容信息和超链信息给出了一种基于分类方法的WEB社群挖掘算法。
Algorithm should build text expressing matrix and classification expressing matrix according to Chinese character features in text, and build classification matrix through using LLS Line...
算法根据文本中汉字的特征建立文本表示矩阵和类别表示矩阵，并通过线性最小二乘算法形成分类矩阵。
Algorithm should build text expressing matrix and classification expressing matrix according to Chinese character features in text, and build classification matrix through using LLS Line...
算法根据文本中汉字的特征建立文本表示矩阵和类别表示矩阵，并通过线性最小二乘算法形成分类矩阵。
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