深层自动编码机的文本分类算法改进  

Text-classification algorithm improvements based on deep autoencoder

作　　者：胡侯立 魏维[1] 谢青松[1] 

机构地区：[1]西安通信学院,西安710106

出　　处：《计算机应用研究》2015年第4期992-995,共4页Application Research of Computers

摘　　要：自动编码机作为一种新兴的深层神经网络学习算法,在高维数据的降维和图像重构中取得了很好的效果。针对该方法在文本分类中重构出大量的对学习没有帮助的含噪数据,提出一种利用原型数据监督学习的改进模型,称做深层原型自动编码机,该方法改善了无监督学习的不足。并且,通过建立多个实例对应一个原型模型,可以大大降低算法对于原型数量的需求,提升了算法的运行效率,而且更加有利于原型学习在多种不同的数据上展开。实验证明该方法可以增加文本分类的准确率。As a new neural network algorithm,deep autoencoder achieved significant results in dimensionality reduction and image reconstruction at high-dimensional level. This paper introduced autoencoder in text classification,and found that this method could reconstruct a lot of noise data which was helpless to learning data. So it presented an improvement model using supervised learning by prototype data,which was called deep prototype autoencoder. This method improved the lack of unsupervised learning. Moreover,this approach created a model that multiple instances corresponding to one prototype data,greatly reduced the number of prototypes that algorithm needed,and enhanced the operational efficiency of the algorithm,while was more conducive to prototype learning in a variety of data. Experiments show that this method can increase the accuracy of text classification.

关 键 词：自动编码机 无监督学习 深层原型自动编码机 原型分类器 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]