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region latent semantic 区域潜在语义
A variation of LSI is called probabilistic latent semantic. 集成电路是一种变异称为概率潜在语义。
This model can capture more latent semantic structure information than LSC. 该模型比LSC模型能更好地表示文档空间的潜在语义结构信息。
This method is an adaptation of the latent semantic indexing method originally used to index text documents. 这种方法是一种适应的潜在语义索引法原本用于文本索引。
This method provides the complete name of the latent semantic indexing model in a string format. 这种方法提供了完整的名字在一连串潜在语义索引模型格式。
Latent semantic indexing is a type of technology that works to understand what a page is about. 潜在语义索引技术是一种能够真正体会到作品即将一页。
Abstract The basic theory and its features about Latent Semantic Indexing(LSI) are analyzed. 摘要分析潜在语义索引的基本原理及其特点。
Latent Semantic Analysis (LSA) is a theory and method of acquisition and representation of knowledge. 摘要潜在语义分析(LSA)是一种知识提取和表示的理论和方法。
A new latent semantic difference model is proposed on the base of various LSI models. 通过分析潜在语义索引的各种模型提出一种新的潜在语义差异模型。
A novel method, constrained non-negative matrix factorization, is presented to capture the latent semantic relations. 提出一种获取在语义的受限非负矩阵分解方法。
A new arithmetic based on Latent Semantic Analysis Model and domain ontology was proposed to summarize the document. 介绍了一种以潜语义分析模型为基础，辅之以领域本体的文档自动摘要算法。
Latent Semantic Indexing is going to change the search engine game; you will need to change your seo efforts to pay off big time. 潜在语义索引的搜索引擎都不会改变游戏;你将需要更改你的努力汉城大内还清。
Latent semantic indexing is a process by which you can determine the subject matter of a web page without relying on specific keywords. 潜在语义索引是一个过程,其中你能确定一个主题网页无具体依赖关键词。
To solve this problem, we propose a new text classification model: Latent Semantic Classification (LSC) model by extending LSI model. 针对上述问题,在扩展LSI模型的基础上,我们提出了一种新的文本分类模型:潜在语义分类模型(Latent Semantic Classification:LSC)。
On the aspectof dissatisfying the independence of text vector asthe synonymy and polysemy of words, the model of latent semantic indexing is presented. 在因词语的同义和多义,不能满足文档向量相互独立方面,提出潜在语义索引模型。
Latent Semantic Index (LSI) was used to select text feature and then Boosting algorithm was proposed to integrate fuzzy classification. 首先采用潜在语义索引(LSI)对文本特征进行选择；
Aimed at the main challenges of recommender system, this thesis based the research on the pLSA (probabilistic Latent Semantic Analysis) model. 针对上述问题，本文对基于模型的推荐算法进行了初步的探索，在前人工作的基础上，研究了基于概率隐含语义分析(probabilistic Latent Semantic Analysis; pLSA)在推荐系统中的应用和实现技术，取得的主要研究成果如下： 1.;通过对用户、社区、推荐对象三者相关关系的分析，发现了社区兴趣相对稳定的特点。
The new method establishes vector space model of term weight according to the theory of latent semantic index, and may eliminate disadvantageous factors. 该方法应用lsi理论来建立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.
The search engine ranking for a particular website will have to pass several processes in the latent semantic indexing based search engine optimization. 搜索引擎排名为某网站将通过几个程序在潜在语义索引的搜索引擎优化。
The statistical characteristics of dimensionality in latent semantic analysis LSA space were studied to realize automatic document clustering under different concept levels. 另外,在基于潜在语义分析的文档聚类算法中,采用文档自检索矩阵的行向量,代替低维文档向量作为聚类对象,获得了更好的聚类准确率。