KNN算法是一种常用的效果较好的文本分类算法。
KNN algorithm is a common and effective text categorization algorithm.
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.
对KNN文本分类算法的理论研究和实际应用起了指导作用。
It plays an instructional role in academic study and practical application of KNN text classification algorithm.
提出了一种基于K近邻(KNN)原理的快速文本分类算法。
This paper presents a fast text classification algorithm based on KNN (K Nearest Neighbor).
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。
Two important research directions of text classification are: feature selection method and text classification algorithm.
提出并实现了一种结合前馈型神经网络和K最近邻的文本分类算法。
This paper put forward and carried out a text classification method using feed-forward neural network and K-nearest neighbor.
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。
The performance of text categorization algorithm based on centroid is poor when the documents are dispersive or existing more than one peak value.
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。
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.
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。
Aiming at this problem, this paper proposes an improved text categorization algorithm whose performance is higher than classical categorization algorithm based on centroid.
实验结果表明，迭代s VM算法分类精度高于传统的SVM文本分类算法，具有较好的性能。
The experiment results show that the iterative SVM classifier achieves better performance than the SVM text classifier.
该文针对中文科技论文文本特殊的文体格式和语言风格进行了系统地研究，并提出了基于层次分类模型的文本分类算法。
In this paper, we construct firstly the interval estimates of variance components in the two-way model, depending on corresponding sums of squares from the analysis of variance.
对于文本分类的核心训练算法，给出主要步骤的推导过程；
The main deducing steps are presented in the core-training algorithm 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。
A reduction algorithm based on rough set is improved and then applicated to extract the rules of text categorization.
现在有很多文本分类的算法，在不同的领域里取得了较好的效果。
Now many algorithms of text classifier make good effects in different domains.
在对这些因素进行分析之后，本文提出了一种基于文本分类的网页排序算法。
After the overall analysis, this paper proposed a new algorithms for page ranking, which is based on Text Categorization.
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。
The results of experiment show that the improved algorithm advances the precision of text classification, and reduces the requirement of training scale.
构建一个分类准确而且稳定的文本分类器是文本分类的关键，很多学者提出了不同的文本分类器模型和算法。
Constructing an accurate and stable text classifier is a key to text categorization. Many researchers put forward various text classifier models and algorithms.
论文针对KNN这种常用的文本分类方法，分析了什么是它的典型样本，提出了一种基于密度的样本选择算法。
In the paper, what is the typical sample of KNN is analyzed, and a method of samples selection based density is presented.
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。
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.
实验结果表明，PKNN算法可以明显提高KNN算法的效率，PKNN算法的原理决定其适合大容量高维文本分类。
Results of the experiment indicated the PKNN enhance efficiency of text classification, and the PKNN is especially effective in large high-dimensional text categorization.
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。
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.
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。
Moreover, S-TFIDF algorithm is as efficient as TFIDF algorithm, which implies it is competent for large scale text categorization task.
对文本分类系统的系统结构、预处理、特征提取、训练算法、分类算法等进行了详细介绍。
The text preprocessing, feature selection, training algorithm, and recognition method are described in the paper.
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。
Using rough set of the final value reduction algorithm for text classification rules extraction, thus gained the final text classification rules.
在得到特征集后，使用覆盖算法作为文本分类器进行学习。
After getting the feature set, USES cover algorithm as a text classifier to study.
文本分类器对于学习算法和分类的结果都是至关重要的一步。
Text classifier is a vital part not only for studying algorithm, but also for the result ofclassification.
分类部分，论文在理论上分析了文本分类采用支持向量机技术的优点，对两种具体的SVM算法-C-SVC和V-SVC进行了研究并利用实例进行验证。
The two classical SVM algorithms-C-SVC algorithm and S-SVC algorithm have been done more research and the two algorithms performance has been compared by using practice data.
这些算法和模型对今后研究文本分类以及其它文本处理问题将有很大的参考价值和借鉴作用。
The algorithms and models presented in this dissertation will be valuable for future studies in text classification and other fields in text processing.
然后，介绍了传统的基于关键字的向量空间模型的文本分类的几个重要阶段，并着重介绍了其中的文本表示的相关技术和两种经典分类算法。
Then, this paper eliminates ambiguity of word meanings in text by WordNet. A representation of text based on concept is proposed later, and has been also applied to classification in SVM and KNN.
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。
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.
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