中文摘要：

SVM分类算法处理高维数据具有较大优势，但其未考虑语义的相似性度量问题，而LDA主题模型可以解决传统的文本分类中相似性度量和主题单一性问题。为了充分结合SVM和LDA算法的优势并提高分类精确度，提出了一种新的LDA—wSVM高效分类算法模型。利用LDA主题模型进行建模和特征选择，确定主题数和隐主题一文本矩阵；在经典权重计算方法上作改进，考虑各特征项与类别的关联度，设计了一种新的权重计算方法；在特征词空间上使用这种基于权重计算的wSVM分类器进行分类。实验基于R软件平台对搜狗实验室的新闻文本集进行分类，得到了宏平均值为0．943的高精确度分类结果。实验结果表明，提出的LDA—wSVM模型在文本自动分类中具有很好的优越性能。

英文摘要：

SVM algorithm has great advantages in dealing with high dimensional data, but it does not consider the problem of semantic similarity measurement. However, LDA topic model can solve the problem of： similarity measurement and single theme. In order to get more precise classification and make use of the advantages of SVM and LDA, this paper proposed a new efficient method. Firstly,it studied on LDA topic model for modeling and feature selection in order to determine the number of hidden topic number and topic-document matrix. Secondly, it proposed a new method for calculating the weights which consid- ered the features and categories of correlation based on classical weight calculation. Finally, it applied this new method to the following SVM classifier （wSVM）. The experiments were based on R software for categorization of the data obtained from So- gou laboratory. The experimental results are with a high accuracy of macro_F1 0. 943. It verifies LDA-wSVM model has superi- ority in text categorization.