AN Information Text Classification Algorithm Based on DBN - LU Shu-bao (Journal of Harbin University of Science and Technology, 2017)
Aiming at the problem of low categorization accuracy and uneven distribution of the traditional text classification algorithms，a text classification algorithm based on deep learning has been put forward. Deep belief networks have very strong feature learning ability，which can be extracted from the high dimension of the original feature，so that the text classification can not only be considered，but also can be used to train classification model. The formula of TF-IDF is used to compute text eigenvalues，and the deep belief networks are used to construct the classifier. The experimental results show that compared with the commonly used classification algorithms such as support vector machine，neural network and extreme learning machine，the algorithm has higher accuracy and practicability，and it has opened up new ideas for the research of text classification.