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   document categorization 的翻译结果: 查询用时:0.188秒
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document categorization
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  文档分类
     Hierarchical Document Categorization Based on Fisher Linear Discriminant
     基于Fisher线性判别式的层次文档分类
短句来源
     Product design knowledge document categorization based on decision tree & SVM
     基于决策支持向量机的产品设计知识文档分类研究
短句来源
     Hierarchical Chinese document categorization
     层次化中文文档分类
短句来源
     To categorize documents hierarchically according to their topics,the thought of Fisher linear discriminant is utilized to get positive feature words and negative feature words in each category, and the algorithm of a hierarchical document categorization is given based on Fisher linear discriminant (HDCF).
     将文档按照主题进行层次分类,利用Fisher线性判别式的思想来提取每一类的正特征词和负特征词,给出基于Fisher线性判别式的层次文档分类算法(HDCF)。
短句来源
     Chinese Document Categorization without Dictionary Support and Segmentation Processing
     无需词典支持和切词处理的中文文档分类
短句来源
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  “document categorization”译为未确定词的双语例句
     A software for automatic Chinese web document categorization was developed which is the base for future research.
     本课题开发的中文WEB文档自动分类工具有较好分类准确度,也为进一步研究打下了基础。
短句来源
     The Application of Multilayer Document Categorization in User's Preference Mining and Processing
     多层次文档归类在挖掘及处理用户偏好中的应用
短句来源
     Study on Feature Selection of Chinese Document Categorization
     中文文本分类中特征提取算法研究
短句来源
     At the realization of the system, we analyze the integral structure and working principle of our system at first. Then, we show the relationship among tables in core database. Lastly, we study automatic document categorization algorithm and propose algorithm descriptions and experiment results of Chinese language segmentation, schema matching of paper titles andclustering.
     在系统实现部分分析了系统的整体结构和工作原理,介绍了系统核心数据库中各表的联系,最后重点研究了文档自动分类算法,给出了汉语分词算法、论文标题模式匹配算法、聚类算法的算法描述及实验结果。
短句来源
     Finally, The text analyzes a document categorization model based on SVM.
     最后详细分析研究了一个基于SVM的文本分类器模型。
短句来源
  相似匹配句对
     Hierarchical Chinese document categorization
     层次化中文文档分类
短句来源
     Hierarchical Chinese document categorization was researched.
     对层次化中文文档分类进行了研究。
短句来源
     the Format of Document
     文件格式精解
短句来源
     19 categorization;
     紓紛矠物品分类;
短句来源
     Attention for E-document
     关注电子文件
短句来源
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  document categorization
Document categorization and keyword assignment prior to entry is optional, thus allowing clerical personnel to maintain both the literature collection and the bibliographic data base.
      
Although considerable research has been conducted in the field of hierarchical document categorization, little has been done on automatic generation of topic hierarchies.
      
In this paper we report an investigation into the learning of semi-structured document categorization.
      
Learning Semi-Structured Document Categorization Using Bounded-Length Spectrum Sub-Sequence Kernels
      
This paper reports our comparative evaluation of three machine learning methods, namely k Nearest Neighbor (kNN), Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM) for Chinese document categorization.
      
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In this paper, a Chinese document categorization system without dictionary support and segmentation processing is developed, in which the N gram information instead of Chinese words is used so that the classifier can shake off the support of dictionaries and segmentation processing and subsequently become domain and time independent, and an open architecture is adopted to facilitate functional expansion and performance improvement. Experimental results show that it can achieve satisfying categorization...

In this paper, a Chinese document categorization system without dictionary support and segmentation processing is developed, in which the N gram information instead of Chinese words is used so that the classifier can shake off the support of dictionaries and segmentation processing and subsequently become domain and time independent, and an open architecture is adopted to facilitate functional expansion and performance improvement. Experimental results show that it can achieve satisfying categorization performance.

报道了一个无需词典支持和切词处理的中文文档分类系统 .其特点是利用 N - gram信息进行中文文档分类 ,使中文文档分类摆脱了对词典和切词处理的依赖 ,实现了中文文档分类的领域无关性和时间无关性 ;采用开放的体系结构使文档分类系统易于功能扩充和性能完善 .测试结果表明该系统具有令人满意的分类性能 .

A new idea that utilizes the adjacent Chinese character pairs information to categorize Chinese documents is proposed so that the classifiers can shake off the requirements of dictionaries and segmentation processing and subsequently be domain and time independent. Such a Chinese documents categorization system is implemented on the basis of Naive Bayes and kNN methods, and the experimental results show that it can achieve satisfying categorization performance.

利用中文文本的N gram信息进行中文文档分类 ,使中文文档分类摆脱了对词典和切词处理的依赖。实验结果表明 ,该方法能够取得令人满意的分类效果

Hierarchical Chinese document categorization was researched. Two measures to classify documents hierarchically were taken. One was to use Bayes classifier according to the structure of document categories. Another was to use KNN classifier and classifier based on Boosting by dividing the large categories into the small ones. Experiment results show that hierarchical categorization does better than plane categorization.

对层次化中文文档分类进行了研究。采用两种途径进行层次化中文文档分类:第一是按照文档类别的层次结构,将一个大的分类问题分化成若干个小的分类问题,用Bayes分类方法分别建立分类器;第二是将层次化分类问题看成是一个更普遍的多类别、多标注分类问题进行求解,分别利用KNN和基于Boosting的分类方法进行层次化中文文档分类。测试结果显示层次化分类比平面分类能够取得更好的分类效果。

 
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