向量空间模型

基于两层向量空间模型和模糊FCA本体学习方法

An Ontology Learning Method Based on Double VSM and Fuzzy FCA

本文针对向量空间模型存在的两个重要不足，分别进行了改进。

In view of the two important shortages in VSM , this paper points out the improved measure differently .

基于多层向量空间模型的Web信息检索方法

A Web Information Retrieval Method Based on Multilayer Vector Space Model

利用向量空间模型（vectorspacemodel）进行网页文档表示，然后利用线性过滤器对网页文档进行过滤。

Based on Vector Space Model filtering the web document according to adaptive user model .

国内主要研究基于统计的方法，例如向量空间模型的摘要系统vectorspacemodel。

Main research is based on statistical methods , such as the summarization based on Vector Space Model .

分析向量空间模型的原理及构建方法，对向量空间模型进行扩展，提出Web服务向量空间模型。

Analyses the principle of VSM . And extends it , puts forward Web services vector space model ( WSVSM ) .

本文结合W亡b信息表示的特点，提出了一种N层向量空间模型。

Using the character of denotation of Web information , an N-level vector space model is proposed .

传统的信息检索主要采用向量空间模型计算相关度，该模型也多用于Web信息检索。

Traditional information retrieval mainly used vector space model , which is also used in Web information retrieval , to calculate the relevance .

K近邻算法是基于向量空间模型的最好的文本分类算法之一。

KNN ( K nearest neighbors ) is one of the best text categorization algorithms based on Vector Space Model .

IDS系统中基于向量空间模型的异常检测

Anomaly Detection Based on VSM in IDS

相关语句抽取部分的相似度计算使用了N元模型和向量空间模型。

In candidate sentences selection module , the methods we used to compute the similarity between sentences and query are N-gram modal and Vector Space modal .

结合HTML标记权重信息建立向量空间模型，弥补了特征项在文本集合中分布的差异。

Vector space model is constructed with HTML tag weights , which offset the distribution differences of text terms .

目前，一些比较成熟的文本分类算法已经被应用到了文本分类中，但它们大都是基于向量空间模型（vectorspacemodel）的。

At present , some comparatively mature text classification algorithm has been applied to text classification , but most of it is based on vector space model ( Vector Space Model ) .

系统通过使用向量空间模型来建立用户兴趣模型，并通过Agent来学习用户的兴趣，动态地修改用户兴趣模型，使其能够反映用户当前的兴趣。

The system builds user profiles using Vector Space Model ; modifies user profiles through learning the users ' interests by agent to reflect user 's interests in time .

分析了Agent和一般信息检索模型的特征，根据Web网页信息检索的特点，综合布尔逻辑模型和向量空间模型的优点，提出了一种基于Agent的智能、主动、自适应超文本网页信息检索模型。

With the analysis of the features of Agent and the Web information search , this paper presents an intelligent self-adaptive model for hyper-text homepage information search based on Agent .

首先应用向量空间模型表示Web文档信息，然后通过矩阵的奇异值分解来进行信息过滤和潜在语义索引；

Firstly , Web documents , which are denoted by vector space model reduced document feature set . Then , information filtering and latent semantic indexing are conducted by singular value decomposition of matrix .

对XML文档的信息检索技术进行了研究，提出了一种在向量空间模型中自动界定信息单元的检索方法。

This paper introduces XML information retrieval technology based on Vector Space Model and presents a retrieval method which can identify suitable information unit for retrieval in vector space automatically .

KNN（K-NearestNeighbour）是向量空间模型中最好的文本分类算法之一。

K - Nearest Neighbour is one of the best text categorization algorithm s.

本文在N层向量空间模型和主题树模型的基础上，通过分析两个模型的分层特性，提出了一种Web主题检索算法。

In this paper , authors analyze the characteristic delimitation of the N-level vector model and topic-specific tree model , and propose a novel kind of topic-specific retrieval algorithm based on the two models .

使用LDA建模，获得文本与隐含类别之间的概率分布矩阵，以此将文本来表示成概率分布的向量空间模型。

And the probability distribution matrix is used as a vector space to present the texts .

本文基于传统搜索引擎Google的基础上，实现了个性化的搜索。论文重点阐述了基于向量空间模型的个性化搜索系统的设计和实现过程。

Based on traditional search Engine-Google , This paper implemented the function of personalized search system and elaborated the design and realization of personalized search system which using Vector Space Model .

接下来从设计思想入手，采用向量空间模型和BP神经网络算法作为文档智能分析的手段，提出了一个实现网络智能过滤的系统架构。

Starting with the design thought , we use vector space models and BP neural networks as the way of intelligent text analysis , and put forward a system frame realizing intelligent network filtering .

向量空间模型（vectorspacemodel）将每篇文章的处理转化为高维向量空间的向量计算，每一个分量表示一个词元权重，也就是把每篇文章的处理转化为了向量的计算。

Vector space model ( Vector Space Model ) turns every article into a high dimensional vector space vector calculation , each component represents a term weights , that is , to transform the procession of every article into the calculation of vector .

N层向量空间模型将文档按照重要程度划分成N层，对每一层的检索单元分别赋予不同的权重，相比传统TF-IDF方法，更能体现文档的特征，能够更好地描述文档。

The term weight vectors are defined according to the text paragraphs ' context . The N-Level vector model can distinguish the documents more efficiently than the classical TF-IDF method .

本文首先针对XML文档的内容信息，从信息检索原理、数学描述、检索模型等方面较全面地研究了传统的文档信息检索技术，设计并实现了一个基于向量空间模型的内容检索试验系统。

First , this thesis has studied classic information retrieval for document from information retrieval theory , description by math , retrieval model and so on , designed and implemented a content retrieval experimental system based on vector space model .

本文首先分析了Web页面的组织特点，对文本自动分类中使用到的向量空间模型、分词、特征选择等关键技术进行了深入的探讨，并实现了一个多项式朴素贝叶斯分类器对中文网页进行分类。

We first analysis the characteristic of HTML documents , then discuss the key technique of automatic text classification , including Vector Space Model , Chinese word segmentation , text feature selection , and implement a multinomial NaiVe Bayes classifier to classify Chinese Web page .

该方法应用LSA理论来构建文本集的向量空间模型，在词条的权重中引入了语义关系，消减了原词条矩阵中包含的噪声因素，从而更加突出了词和文本之间的语义关系。

This method establishes vector space model of term weight by the theory of latent semantic analysis , and eliminates disadvantageous factors .

利用向量空间模型表征用户特征，再用支持向量机将Folksonomy用户二分类。

Vector space model , the characterization of user characteristics , and then support vector machine Folksonomy user two classification .

通过对一般文本的表示模型的分析，并结合基于XML的军用信息的特点，对向量空间模型的权值计算方法进行了改进，并在预处理阶段提出了一种新的最大匹配分词算法。

Through analyzing the denotation model of general text and combining the character of Ml based on XML , the paper improves the original weight calculation method of VSM ( vector space model ) and presents the novel MM ( Maximal Match ) algorithm .

以中文切词、英文Stemming操作和HTML标记分析加权为基础的索引策略能够较好的表示网页的内容，同时为基于向量空间模型的相似度计算奠定了基础。

Based on truncation of Chinese vocabulary and HTML mark analyze , the index tactics could express the content of webpage better , and build foundation for similarity calculation of vector space model .