Automation, Control and Intelligent Systems

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Layered Feature Recognition Algorithm Based on Combined Convolution

Received: Nov. 19, 2019    Accepted: Dec. 11, 2019    Published: Dec. 24, 2019
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Abstract

In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.

DOI 10.11648/j.acis.20190704.11
Published in Automation, Control and Intelligent Systems ( Volume 7, Issue 4, August 2019 )
Page(s) 99-110
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Pattern Recognition, Convolution Algorithm, Single Sample, Face Recognition, Handwritten Digital Recognition

References
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  • APA Style

    Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, et al. (2019). Layered Feature Recognition Algorithm Based on Combined Convolution. Automation, Control and Intelligent Systems, 7(4), 99-110. https://doi.org/10.11648/j.acis.20190704.11

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    ACS Style

    Shuduo Zhao; Xu Han; Jin Xu; Haiyun Chen; Guanqin Feng, et al. Layered Feature Recognition Algorithm Based on Combined Convolution. Autom. Control Intell. Syst. 2019, 7(4), 99-110. doi: 10.11648/j.acis.20190704.11

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    AMA Style

    Shuduo Zhao, Xu Han, Jin Xu, Haiyun Chen, Guanqin Feng, et al. Layered Feature Recognition Algorithm Based on Combined Convolution. Autom Control Intell Syst. 2019;7(4):99-110. doi: 10.11648/j.acis.20190704.11

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  • @article{10.11648/j.acis.20190704.11,
      author = {Shuduo Zhao and Xu Han and Jin Xu and Haiyun Chen and Guanqin Feng and Chenxin Ma and Wenhao Zhou},
      title = {Layered Feature Recognition Algorithm Based on Combined Convolution},
      journal = {Automation, Control and Intelligent Systems},
      volume = {7},
      number = {4},
      pages = {99-110},
      doi = {10.11648/j.acis.20190704.11},
      url = {https://doi.org/10.11648/j.acis.20190704.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.acis.20190704.11},
      abstract = {In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Layered Feature Recognition Algorithm Based on Combined Convolution
    AU  - Shuduo Zhao
    AU  - Xu Han
    AU  - Jin Xu
    AU  - Haiyun Chen
    AU  - Guanqin Feng
    AU  - Chenxin Ma
    AU  - Wenhao Zhou
    Y1  - 2019/12/24
    PY  - 2019
    N1  - https://doi.org/10.11648/j.acis.20190704.11
    DO  - 10.11648/j.acis.20190704.11
    T2  - Automation, Control and Intelligent Systems
    JF  - Automation, Control and Intelligent Systems
    JO  - Automation, Control and Intelligent Systems
    SP  - 99
    EP  - 110
    PB  - Science Publishing Group
    SN  - 2328-5591
    UR  - https://doi.org/10.11648/j.acis.20190704.11
    AB  - In recent years, the deep learning algorithms were gradually understood and accepted. It needs to take too many samples to train. Since the implementation of deep learning algorithm, it seems that the past classical algorithms have become gloomy. In this paper, we get an intelligent pattern recognition model by combining some classical algorithms in the past and extrapolating the convolution algorithm. This new model is based on a single regular sample, with its advanced generalization capabilities far beyond those of deep learning algorithms. Experimental results on MNIST, QMNIST, CMU PIE and Extended Yale B databases indicate that the proposed model is better than the related methods as compared with.
    VL  - 7
    IS  - 4
    ER  - 

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Author Information
  • School of Information, Southwest Petroleum University, Nanchong, China

  • School of Information, Southwest Petroleum University, Nanchong, China

  • School of Information, Southwest Petroleum University, Nanchong, China

  • School of Information, Southwest Petroleum University, Nanchong, China

  • School of Information, Southwest Petroleum University, Nanchong, China

  • School of Information, Southwest Petroleum University, Nanchong, China

  • School of Information, Southwest Petroleum University, Nanchong, China

  • Section