• In network information retrieval, based on document vector space, class, cluster, ranking and relevance feedback need to compute similarity.

    网络信息检索中基于文档向量空间分类聚类排序相关性反馈需要计算相似度

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  • It monitors the free resource of the system and adjusts a sliding time window on news stream accordingly. This way, the dimensions of document vector and user profile vector change dynamically.

    方法监测系统资源使用情况,通过调整新闻时间窗口文档向量维度用户模型关键词维度方式来动态调整相似度计算的运算量。

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  • The function in Listing 20 returns a vector of vectors. This is because each document could generate zero to many map entries, and each map entry is represented as a vector.

    清单20中的函数返回矢量矢量是因为每个文档都可能生成多个映射条目每个映射条目表示一个矢量。

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  • As an example, you can embed a separate XML format to represent scalable vector graphics within the XML document.

    作为示例可以XML文档中嵌入单独的xml格式表示可伸缩矢量图形

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  • In Part 2, I'll show you how to load, manipulate, or synthesize a DOM document, and create the same bar graph using Scalable Vector Graphics (SVG).

    第2部分,展示如何装载操作或者合成一个DOM文档,并用可伸缩矢量(Scalable VectorGraphics,SVG)创建同样条形图。

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  • With views, everything that you emit as a key (the first item in the inner vector) is internally paired with the ID of the document that the key came from.

    使用视图时作为key(内部矢量中的第一条目)发出东西都在内部那个key源自的文档ID配对

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  • Parallel text document to vector conversion using LLR based ngram generation

    结合基于LLR的ngram生成算法并行处理文本文档向量转换

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  • Support multiple publishing formats such as PDF (Portable Document format supported by Adobe Acrobat), SVG (Scalable Vector Graphics for images), RSS (Rich Site Summary for Web portals), and more.

    支持例如pdf (adobeAcrobat支持的“可移植文档格式”)、SVG(用于图像的“可伸缩向量图形”)、RSS(用于web门户的“丰富站点摘要”)等多种发布格式

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  • It constructs document feature vector of subject and Keyword separately by using a new method of document feature extraction.

    使用新的文档特征抽取方法构造了文档主题关键字特征向量

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  • IE9 reports a UA string, version vector, and document mode to match the default browser behavior, which is the most standards-compliant mode in IE9.

    IE9报告ua字符串版本向量文档默认浏览器行为模式匹配符合标准模式IE9。

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  • We set up document feature vectors and user interest vector, and do their correlation calculation by using vector space model, finally return the interested results for the user.

    分别构建文档特征向量用户兴趣向量运用向量空间模型相关性计算返回用户兴趣检索结果

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  • In the most categorization algorithms, the text or document is always represented using Vector Space Model.

    后长数文本开类方式空间模型为基本的。

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  • As a result, each document is represented with respect to all such features (all documents have the same number of features) but most of the values will be zero, creating a very sparse vector.

    作为结果每个文档表示相对所有这些特征(所有文件相同数量功能),大部分为零,一个非常稀疏向量生成

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  • Aimed at the problems of document automatic classification, a classification method is proposed based on fuzzy vector space model and RBF network.

    针对文本自动分类问题提出了基于模糊向量空间模型径向基函数网络的分类方法

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  • Portable Document format (PDF) files have increasingly been used as a means to deliver spammers' advertisements, but only rarely has the file format been used as an attack vector.

    PD F文件越来越多地用来作为手段运送垃圾邮件广告只有少数文件格式被用来作为攻击向量。

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  • The order in which the terms appear in the document is lost in the vector space representation.

    词项文档中的顺序会在向量空间表示忽略

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  • Under the conditions of preserving the meaning of the sample document, a feature vector which can best reflect the content of the sample document and is comparatively brief is found.

    采用演化计算方法,对初步得出文档矢量做进一步的优化处理,在保证原文含义的基础上,找出反映样例文档内容比较简洁特征矢量,并提出新的适应值函数。

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  • So, the traditional KNN arithmetic, clusters training document with highly overlapping word is improved, central vector of cluster is gained.

    为此改进传统KNN算法,将训练文本中相似度大文本合并,称为一,并计算簇的中心向量

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  • After analyzing the disadvantages of the user profile based on-keywords vector in the existing document recommendation system, a novel representation of user profile based on clustering was proposed.

    分析现有文章推荐系统基于关键词向量用户模型表示方法存在不足,提出了基于聚类兴趣点的用户模型表示方法。

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  • The high generalization ability of Support Vector Machine (SVM) makes it especially suitable for the classification of high-dimension data such as term-document.

    支持向量(SVM)高度泛化能力使特别适用于高维数据例如文档分类

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  • The high generalization ability of Support Vector Machine (SVM) makes it especially suitable for the classification of high-dimension data such as term-document.

    支持向量(SVM)高度泛化能力使特别适用于高维数据例如文档分类

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