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.
研究结果认为疾病的中医证候分类和证候要素的提取研究采取潜在变量模型等系列分析方法是适宜的。
The result indicated that the analysis methods of variable models were suitable for the research on disease classification according to TCM syndromes and syndrome elements extraction.
第一，提出一种有监督的潜在语义索引(SLSI)模型降维方法，用于文本分类任务中的特征表示。
The main contributions include: 1 a novel dimension reduction method, Supervised Latent Semantic Indexing SLSI, was proposed to represent documents for text classification tasks.
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