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- 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。
- 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。
- 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。
- 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。
- 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。
- This paper briefly describes the background of text filtering and puts forward the logic model for Chinese-English cross-language text filtering based on Latent Semantic Indexing. 文章简要地描述了文本过滤的背景，提出了基于潜在语义索引的中英文双语交叉过滤的逻辑模型。
- Text browsing based on Latent Semantic Indexing(LSI)is presented in this paper,and it combines LSI with concept tagging to improve the efficiency of users reading. 它吸取了潜在语义索引和概念标注的优点 ,利用潜在语义索引 ,减少词汇间的“斜交”现象 ,在语义空间上进行项与项、文本与文本、项与文本之间的相似度计算。
- Text browsing based on Latent Semantic Indexing(LSI)is presented in this paper, and it combines LSI with concept tagging to improve the efficiency of users reading. 它吸取了潜在语义索引和概念标注的优点，利用潜在语义索引，减少词汇间的“斜交”现象，在语义空间上进行项与项、文本与文本、项与文本之间的相似度计算。
- 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；
- 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 experimental results show that Kernel PCA works almost as well as Latent Semantic Indexing (LSI) over Reuters21578 while the micro-F1 of Kernel PCA is 2% higher than that of LSI over Chinese 863 Evaluating corpus. 实验结果表明,核主成分分析在英文语料reuters21578上达到了潜在语义索引分类性能,而在中文863评测语料上微平均F1值比潜在语义索引高2%25。
- LS-SVM first analyzed text using LSI (latent semantic indexing), which achieved semantic-based character reduction and text express, then combined SVM to complete semantic-based topic tracking. 然后将隐含语义文本表示的结果输出给SVM进行主题追踪，从而实现从语义层次上的新闻主题追踪。
- Abstract LSI (Latent semantic Indexing) brings the traditional information retrieval into the semantic retrieval, which improves the performance of information retrieval system effectively. 摘要潜在语义标引（LSI）的提出，使信息检索由传统的基于关键词的检索开始进入基于概念的语义检索阶段，有效提高了信息检索系统的性能。
- LSI Latent Semantic Indexing Model LSI潜在语义信息检索模型
- Probabilistic Latent Semantic Indexing (PLSI) 概率潜在语义索引
- A Pilot Study of Latent Semantic Indexing for Text Retrieval 文本检索的潜在语义索引法初探
- The index is at the back (of the book). 索引在(书的)末尾。
