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Neural Computing and Applications

, Volume 29, Issue 1, pp 61–70 | Cite as

Text classification based on deep belief network and softmax regression

  • Mingyang Jiang
  • Yanchun Liang
  • Xiaoyue Feng
  • Xiaojing Fan
  • Zhili Pei
  • Yu Xue
  • Renchu Guan
Recent advances in Pattern Recognition and Artificial Intelligence

Abstract

In this paper, we propose a novel hybrid text classification model based on deep belief network and softmax regression. To solve the sparse high-dimensional matrix computation problem of texts data, a deep belief network is introduced. After the feature extraction with DBN, softmax regression is employed to classify the text in the learned feature space. In pre-training procedures, the deep belief network and softmax regression are first trained, respectively. Then, in the fine-tuning stage, they are transformed into a coherent whole and the system parameters are optimized with Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. The experimental results on Reuters-21,578 and 20-Newsgroup corpus show that the proposed model can converge at fine-tuning stage and perform significantly better than the classical algorithms, such as SVM and KNN.

Keywords

Deep belief networks Softmax model Restricted Boltzmann machines L-BFGS Feature learning 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61163034, 61373067, 61572228, 61272207, 61472158), the 321 Talents Project of the two level of Inner Mongolia Autonomous Region (2010), the Inner Mongolia Talent Development Fund (2011), the Natural Science Foundation of Inner Mongolia Autonomous Region of China (2016MS0624), the Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region (NJZY16177), and Science and Technology Development Program of Jilin Province (20140101195JC, 20140520070JH, 20160101247JC).

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.College of Computer Science and TechnologyInner Mongolia University for the NationalitiesTongliaoChina
  3. 3.Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Jilin UniversityZhuhaiChina
  4. 4.College of Mechanical EngineeringInner Mongolia University for the NationalitiesTongliaoChina
  5. 5.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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