本文从理论上探讨了向量空间模型及其改进模型在专题文献过滤中的相关算法。
This article discussed the algorithms of literature filtering based on vector space model (VSM) and other improved models.
该文介绍了一种文本过滤算法，该算法把基于空间向量模型的主题分类算法与基于支持向量机文本态度分类结合起来。
This paper introduces a text filtering system merging topic classification based on vector space model and sentiment classification based on support vector machine.
综合用户显式反馈和用户隐式反馈的优点，本文还提出了一种用户建模的算法，即基于向量空间模型的混合建模方法。
Integrating the merits of user obvious feedback and user hiding feedback, a new user establishing model algorithm, namely a mix establishing model method based on vector space model is also proposed.
本文介绍了文本自动分类的研究方法，文本的向量空间模型表示。并给出了文档的训练算法和分类算法。
It introduces the method of the text auto-categorization, briefly describes the vector space model expression, and finally gives training arithmetic and categorization arithmetic of the text.
针对向量空间模型在文档相似度量方面的局限，提出了基于计算公共子串的文档相似度量算法。
In respect to the limitation of document similarity measuring based on VSM, this paper put forward an algorithm based on public substring of strings.
然后，本文介绍了几种常用的基于向量空间模型的文件分类算法，同时描述了具体的算法步骤。
The second part is the introduction to several common File Categorization methods and the algorithms are presented in detail.
在传统过滤算法的基础上，本文提出一种基于向量空间模型的自适应过滤算法。
Based on tradition filtering algorithm, an adaptive filtering algorithm based on vector space model is proposed in the paper.
基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。
Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.
基于向量空间模型表示用户模型，采用重心向量分类算法建立用户模型。
The user model is expressed in form of vector space model. It's built using centroid-based classification method.
本文提出了两个新的图上关键字搜索算法，使用了现代信息检索技术中的向量空间模型和随机游走模型来解决以上缺陷，使得查询结果更具语义信息。
In this paper, two novel algorithms are introduced, which employ the vector space model and random walk model to address the drawbacks above and make the results more semantical.
基于重启型随机游走的图上关键字搜索算法，在重启型随机游走模型的基础上加入了向量空间模型。
The first algorithm is based on random walk with restart model, which modifies the model to support vector space model.
该文探讨了基于向量空间模型的文本聚类方法，提出 了一种文本聚类的改进算法——LP 算法。
This paper discusses different Vector Space Model(VSM)-based clustering algorithms and presents an improved text clustering algorithm——Level-Panel(LP)algorithm.
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.
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.
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