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   网页表示 的翻译结果: 查询用时:0.254秒
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  “网页表示”译为未确定词的双语例句
     On page purification module, we describe a web page as a DOM tree, and introduce the number of Chinese punctuation into the weight of page content.
     在网页净化技术说明中,我们将网页表示成一颗DOM树,并首次将中文标点符号数引入到衡量网页正文的权重中,通过对DOM树不断的剪枝,极大的减少了网页的噪音。
短句来源
     The web classification is the problem of automatically assigning electronic text documents to pre-specified categories.
     本文提出了一种 SOFM(自组织特征映射 )与 L VQ(学习矢量量化 )相结合的分类算法 ,利用一种新的网页表示方法 ,形成特征向量并应用于网页分类中 .
短句来源
     The method uses a tree to represent a web page according to HTML tags, and then chooses the node which contains text content by using the number of the Chinese characters in each node of the tree.
     该方法先根据网页中的HTML标记把网页表示成一棵树 ,然后利用树中每个结点包含的中文字符数从中选择包含正文信息的结点。
短句来源
     Especially,the development of the web experiment ActiveX control is dwelled concretely.
     重点阐述网上实验控件的设计方法和在网页表示层调用控件的方法;
短句来源
     The method uses DOM tree to represent a web page according to HTML tags.
     先根据网页中的HTML标记把网页表示成一棵DOM树;
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  相似匹配句对
     The Representation of Measures of Fuzziness
     模糊度的表示
短句来源
     The Representations of F-L Sequences
     F-L序列的表示
短句来源
     The Color Design of Web Page
     网页的色彩设计
短句来源
     Creating Web
     网页制作
短句来源
     Application of Virtual Description Model in Web Pages Structured Design
     虚拟表示模型在网页结构化设计中的应用
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This paper presents a new algorithm that combines Support Vector Machine (SVM) and unsupervised clustering. After analyzing the characteristics of web pages, it proposes a new vector representation of web pages and applies it to web page classification. Given a training set, the algorithm clusters positive and negative examples respectively by the unsupervised clustering algorithm (UC), which will produce a number of positive and negative centers. Then, it selects only some of the examples to input to SVM according...

This paper presents a new algorithm that combines Support Vector Machine (SVM) and unsupervised clustering. After analyzing the characteristics of web pages, it proposes a new vector representation of web pages and applies it to web page classification. Given a training set, the algorithm clusters positive and negative examples respectively by the unsupervised clustering algorithm (UC), which will produce a number of positive and negative centers. Then, it selects only some of the examples to input to SVM according to ISUC algorithm. At the end, it constructs a classifier through SVM learning. Any text can be classified by comparing the distance of clustering centers or by SVM. If the text nears one cluster center of a category and far away from all the cluster centers of other categories, UC can classify it rightly with high possibility, otherwise SVM is employed to decide the category it belongs. The algorithm utilizes the virtues of SVM and unsupervised clustering. The experiment shows that it not only improves training efficiency, but also has good precision.

提出了一种将支持向量机与无监督聚类相结合的新分类算法 ,给出了一种新的网页表示方法并应用于网页分类问题 .该算法首先利用无监督聚类分别对训练集中正例和反例聚类 ,然后挑选一些例子训练 SVM并获得 SVM分类器 .任何网页可以通过比较其与聚类中心的距离决定采用无监督聚类方法或 SVM分类器进行分类 .该算法充分利用了 SVM准确率高与无监督聚类速度快的优点 .实验表明它不仅具有较高的训练效率 ,而且有很高的精确度 .

The web classification is the problem of automatically assigning electronic text documents to pre-specified categories. In this paper,we focus on the SOFM algorithm that is derived automatically using a technique based on frequencies of titles and frequencies of

本文提出了一种 SOFM(自组织特征映射 )与 L VQ(学习矢量量化 )相结合的分类算法 ,利用一种新的网页表示方法 ,形成特征向量并应用于网页分类中 .该方法充分利用了 SOFM自组织的特点 ,同时又利用 L VQ解决聚类中测试样本的交迭问题 .实验表明它不仅具有较高的训练效率 ,同时有比较好的查全率和查准率

This paper proposes a statistical approach for extracting text content from Chinese news web pages in order to effectively apply natural language processing technologies to web page documents. The method uses a tree to represent a web page according to HTML tags, and then chooses the node which contains text content by using the number of the Chinese characters in each node of the tree. In comparison with traditional methods, the method neednt construct different wrappers for different data sources. It is simple,...

This paper proposes a statistical approach for extracting text content from Chinese news web pages in order to effectively apply natural language processing technologies to web page documents. The method uses a tree to represent a web page according to HTML tags, and then chooses the node which contains text content by using the number of the Chinese characters in each node of the tree. In comparison with traditional methods, the method neednt construct different wrappers for different data sources. It is simple, accurate and easy to be implemented. Experimental results show that the extraction precision is higher than 95%. The method has been adopted to provide web text data for a question answering system of traveling domain.

为了把自然语言处理技术有效的运用到网页文档中 ,本文提出了一种依靠统计信息 ,从中文新闻类网页中抽取正文内容的方法。该方法先根据网页中的HTML标记把网页表示成一棵树 ,然后利用树中每个结点包含的中文字符数从中选择包含正文信息的结点。该方法克服了传统的网页内容抽取方法需要针对不同的数据源构造不同的包装器的缺点 ,具有简单、准确的特点 ,试验表明该方法的抽取准确率可以达到 95 %以上。采用该方法实现的网页文本抽取工具目前为一个面向旅游领域的问答系统提供语料支持 ,很好的满足了问答系统的需求

 
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