This paper proposed that latent semantic indexing ( LSI) was used for Web text dimension reduction and feature extraction, and then the processed results was clustered by support vector clustering ( SVC). 提出对网页文本提取特征值后,利用潜在语义索引对网页文本降维,采用支持向量聚类(SVC)算法对降维后的特征向量进行聚类,以此进行文本分类。
Research on latent semantic index retrieval model by expanding vector space model 一种扩展的向量空间模型-隐含语义索引模型研究
Latent Semantic Index ( LSI) retrieval model was designed by expanding Vector Space Model. 在深入分析向量空间模型基础上,对其进行扩展,设计了一种隐含语义索引模型&LSI。
Secondly, based on the principal component analysis to the price term structure, this paper withdraws the latent factor which reflects the dynamics of term structure and examines the relation between the latent factor and inflation by the vector auto regression ( VAR) model. 其次,在对价格期限结构进行主成分分析的基础上,本文提取反映商品期货价格期限结构变化的潜在因子,通过向量自回归(VAR)模型,考察价格期限结构的潜在因子与通货膨胀指标的关系。
In the past several years, a new text representation model called Latent Semantic Indexing has been put forward to process semantic side aiming at the defects of the traditional Vector Space Model. 针对向量空间模型表示法的局限性,采用潜在语义索引在语义层面进行处理,是近几年提出的一种文本表示方法。
The low-level weight histogram and high-level semantic attributes are fused together and the latent Support Vector Machine ( SVM) combined with coordinate descent is adopted to find the local optimum of the prediction model instead of the single SVM classifier. 将低层次的权重直方图特征和高层次的动作语义属性融合,采用隐支持向量机结合坐标下降法替代单一支持向量机分类方法求解最终动作识别模型的局部最优解。