Others rely on automatic text classification techniques.
其他方式依赖于自动文本分类技术。
Text preprocessing is the bottleneck of text classification.
文本预处理是文本分类的瓶颈。
Text classification is a key technique for organizing document set in IR.
文本分类是对信息检索中的文档集进行组织的一项关键技术。
SVM for text classification - tutorial on machine learning? How do I get started?
SVM文本分类的机器学习教程？我如何开始？
Text classification and cluster are two important missions of information processing.
文本分类和文本聚类是信息处理中的两个重要工作。
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。
This paper presents an automated text classification model based on projection pursuit regression.
提出了一种基于投影寻踪回归的文本自动分类模型。
The word which is not splitted can get better result of text classification than the splitted word.
同时对哈萨克语单词切分和未切分进行分类对比。得出未切分单词可以得出更好的分类效果。
Text classification on the basis of the contents of the text refers to the text category automatically.
文本分类是指根据文本的内容自动确定文本类别的过程。
In this paper, the Word Segmentation technology of Chinese Text Classification is debated emphatically.
本文对中文文本分类的分词技术进行了着重讨论。
One method of managing the texts efficiently is to classify them, namely, the problem of text Classification.
对文本进行有效管理的方法之一，就是将它们进行系统地分类，即文本自动分类问题。
Based on DragPush strategy, the paper introduces a text classification incremental learning model, named ICCDP.
基于拉推策略的基本思想，该文提出了文本分类的增量学习模型ICCDP。
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。
In text classification, we advance an automatic text classification way based on clustering and Rough Set Theory.
在文本分类方面，本文提出了一种基于聚类和粗糙集理论相结合的文本自动分类方法。
The definition of proximate rule was proposed based on the characteristic of text classification rule extraction.
结合文本分类规则抽取的特点，给出了近似规则的定义。
This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
This article studies the statistical automatic text classification methods in the National Science Digital Library.
本文在国家科学数字图书馆的应用背景下，研究基于统计的自动文本分类方法。
Various text classification methodologies may yield different types of "models," or statistical descriptions of the world.
各种文本分类方法可以生成不同类型的 “模型”，即对真实环境的统计学描述。
Effective dimensionality reduction could make the learning task more efficient and more accurate in text classification.
在文本分类中，有效的维数约简可以提高学习任务的效率和分类性能。
Researches on automatic text classification in Kazakh and develops a system of automatic text classification for Kazakh.
研究哈萨克语自动文本分类，并实现哈萨克语文本自动分类系统。
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。
This paper put forward and carried out a text classification method using feed-forward neural network and K-nearest neighbor.
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。
The research can be applied in information retrieval, information filtering, text classification, webpage classification, etc.
该研究可应用于信息检索、信息过滤、文本自动分类、网页自动分类等应用领域。
Therefore it adjusts to human's mode of thinking and further improves the efficiency, correctness and reliability of text classification.
更符合人类的思维方式，从而进一步提高了文本分类的效率、准确性和可靠性。
In addition, a text classification system based on Vector Space Model is studied and a new method for calculating word weight is proposed.
此外，本文还研究了基于向量空间模型的自动文本分类方法，提出了一个新的词权重计算方法，该方法有效提高了分类精度。
For example, NLTK has a whole framework for text classification using statistical techniques like "naive Bayesian" and "maximum entropy" models.
例如，NLTK有一个完整的框架，用于通过类似于“naiveBayesian”和“maximumentropy”等模型的统计技术进行文本分类。
A text classification system based on improved classification model presented in this paper is used to realize automatic text classification.
提出一种基于改进的分类模型的文本分类系统来实现文本的自动分类。
The rapid growth in the amount of electronic documents brings both great opportunities and real challenges for automatic text classification.
电子文档的飞速增长为自动文本分类提供了巨大的机遇和挑战。
Text orientation recognition has broad application in some field such as information filtering, automatic abstracting and text classification.
文本倾向性识别在信息过滤、自动文摘、文本分类等领域有广泛的应用前景。
Because of its excellent learning ability, SVM has been applied to such fields as Text Classification, Face Recognition, and Figure Recognition.
由于其出色的学习和推广性能，支持向量机已经被应用到许多方面，例如：文本分类、人脸识别、指纹识别等等。
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