【摘要】 【目的】借助深度学习理论,解决传统特征选择方法容易导致特征项不明确、分类精度下降的问题。【方法】对中文新闻文本进行分类时,使用降噪自动编码器构建一个深层网络来学习对文本的压缩及分布式的表示,并在网络最后一层采用SVM算法将其分类到具体的类别中去。【结果】随着样本数目的增大,分类准确率、召回率和F值都在上升,且比KNN算法、BP算法和SVM算法取得了更优的分类效果,平均分类准确率达到95%以上。【局限】数据量依然较小,且并没有完全发挥深度学习并行处理大容量数据的优势。【结论】该方法能提高特征项提取的准确性,并能提高分类效果。更多还原

【Abstract】 [Objective] This paper proposes a new method to improve the classification accuracy of the Chinese news texts with the help of Deep Learning theory.[Methods] We first used the denoising auto encoder to construct a deep network to learn the zipped and distributed representation of the Chinese news texts.Second,we used the SVM algorithm to classify these news texts.[Results] As the number of samples expanding,the precision rate,the recall rate and the F value of the proposed method increased too.The results are better than those of the applications using the KNN,BP and SVM algorithms.The average precision rate was higher than 95%.[Limitations] The data size was relatively small,thus,the proposed method did not fully utilize the parallel data processing capacity of the deep learning technology.[Conclusions] The proposed method improves the performance of applications classifying Chinese news texts.更多还原