潜在语义分析(LSA)是一种知识提取和表示的理论和方法。
Latent Semantic Analysis (LSA) is a theory and method of acquisition and representation of knowledge.
为了提高文本分类的准确性，研究并设计了一个基于潜在语义分析和支持向量机的多类文本分类模型。
A multiclass text categorization model based on latent semantic analysis and support vector machine is researched and designed to enhance the accuracy of categorization.
利用潜在语义分析进行特征抽取，消除多义词和同义词在文本表示时造成的偏差，并实现文本向量的降维。
Using latent semantic analysis to extract feature, the affect of synonymy and polysemy in text representation process is eliminated and the dimension of text vector is reduced.
针对潜在语义分析（LSA）模型的权重更新问题，提出了一种基于贝叶斯理论的自适应权重更新算法ALSAB。
To the weight update of Latent Semantic Analysis(LSA)model, this paper proposes an adaptive weight update algorithm based on Bayesian theory(ALSAB).
本文针对潜在语义分析存在的缺陷，采用概率潜在语义分析的方法构造文本——词语的同现矩阵，使用EM算法进行迭代求解。
In this paper, the defects latent semantic analysis, probabilistic latent semantic analysis using methods to construct the text-the words of co-occurrence matrix, using the em algorithm to solve.
本文针对潜在语义分析存在的缺陷，采用概率潜在语义分析的方法构造文本——词语的同现矩阵，使用EM算法进行迭代求解。
In this paper, the defects latent semantic analysis, probabilistic latent semantic analysis using methods to construct the text-the words of co-occurrence matrix, using the em algorithm to solve.
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