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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 4
1026-1030

Research on batch regularization DBN classification method

Li Beibei
Song Wei
Dai Xin
School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2017.09.0969

Abstract

Aiming at the problem that the deep belief network(DBN) is susceptible to the training parameters during the fine-tune process, this paper proposed a kind of batch normalization DBN classification method(BNDBN). Firstly, this method used unsupervised learning to obtain high-level representation of raw data. Then through the introduction of scale transformation and translation transformation parameters, it processed the output characteristics of each layer by batch normalization. And it fed the post-processing characteristics into the nonlinear transformation activation layer. Finally, it trained and studied the parameters of the affine transformation and the original network by using the stochastic gradient descent method. The BNDBN method reduced the dependence of the gradient on the parameter size, which effectively resolved the problem of changing the value distribution of activation function caused by the change of network parameters and improved the training efficiency. To verify the effectiveness of the proposed method, it selected MNIST handwritten database and the USPS handwritten digital identification library for testing. Compared with the Dropout-DBN, DBN, ANN, SVM and KNN, the results show that the proposed method significantly improves the classification accuracy and had stronger feature extraction ability.

Foundation Support

国家自然科学基金资助项目(61673193)
中央高校基本科研业务费专项资金资助项目(JUSRP51635B,JUSRP51510)
江苏省自然科学基金资助项目(BK20150159)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.09.0969
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 4
Section: Algorithm Research & Explore
Pages: 1026-1030
Serial Number: 1001-3695(2019)04-015-1026-05

Publish History

[2019-04-05] Printed Article

Cite This Article

李蓓蓓, 宋威, 戴鑫. 批量正则化DBN分类方法研究 [J]. 计算机应用研究, 2019, 36 (4): 1026-1030. (Li Beibei, Song Wei, Dai Xin. Research on batch regularization DBN classification method [J]. Application Research of Computers, 2019, 36 (4): 1026-1030. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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