Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

An effective classifier based on convolutional neural network and regularized extreme learning machine

College of Information Engineering, Xiangtan University, Xiangtan, Hunan 411105, China

An effective classifier combining convolutional neural network and regularized extreme learning machine (called as CNN-RELM) is presented in this paper. Firstly, CNN-RELM trains the convolutional neural network (CNN) using the gradient descent method until the learning target accuracy reaches. Then the fully connected layer of CNN is replaced by regularized extreme learning machine (RELM) optimized by genetic algorithm and the rest layers of the CNN remain unchanged. The experiments on different face databases are given to evaluate the performance of CNN-RELM. The experimental results show that CNN-RELM is a feasible classifier and it outperforms CNN and RELM. Due to the uniting of CNN and RELM, CNN-RELM have the advantages of CNN and RELM and it is easier to learn and faster in testing.
  Article Metrics


1. Y. Bengio, Learning deep architectures for AI, Found. Trends Mach. Learn., 1 (2009), 1-71.

2. F. Schroff, D. Kalenichenko and J. Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), United States of America, 2015, 815-823. Available from: https://arxiv.org/pdf/1503.03832.pdf.

3. G. Huang, Z. Liu, L. van der Maaten, et al., Densely Connected Convolutional Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), United States of America, 2017, 4700-4708. Available from: https://arxiv.org/abs/1608.06993.

4. Z. Q. Zhao, P. Zheng, S. T. Xu, et al., Object Detection with Deep Learning: A Review, IEEE Trans. Neural Networks Learn. Syst., (2019), 1-21.

5. Y. L. Cun, L. D. Jackel, B. Boser, et al., Handwritten digit recognition: Applications of neural network chips and automatic learning, IEEE Commun. Mag., 11 (1989), 41-46.

6. I. J. Goodfellow, J. P. Abadie, M. Mirza, et al., Generative Adversarial Networks, Advances in neural information processing systems, 2014, 2672-2680. Available from: https://arxiv.org/abs/1406.2661.

7. G. E. Hinton, S. Osindero and Y. W. Teh, A fast learning algorithm for deep belief nets, Neural Comput., 18 (2006), 1527-1554.

8. Y. Z. Xu, X. J. Yao, X. Li, et al, Target detection in high resolution remote sensing images based on full convolution network, Bull. Surv. Mapp., 1 (2018), 77-82.

9. M. S. Hasan, An application of pre-trained CNN for image classification, 2017 20th International Conference of Computer and Information Technology (ICCIT), 2017. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8281779.

10. X. Qin, Y. Zhou, Z. He, et al., A Faster R-CNN Based Method for Comic Characters Face Detection, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017, 1074-1080. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8270109.

11. R. Ranjan, V. M. Patel and R. Chellappa, HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition, IEEE Trans. Pattern Anal. Mach. Intell., 41 (2019), 121-135.

12. S. Xie and H. Hu, Facial Expression Recognition Using Hierarchical Features with Deep Comprehensive Multipatches Aggregation Convolutional Neural Networks, IEEE Trans. Multimedia, 21 (2019), 211-220.

13. G. Chen, C. Li, W. Wei, et al., Fully convolutional neural network with augmented atrous spatial pyramid pool and fully connected fusion path for high resolution remote sensing image segmentation, Appl. Sci., 9 (2019), 1816.

14. A. Kulikajevas, R. Maskeliūnas, R. Damaševičius, et al., Reconstruction of 3D Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3D Models from ShapeNetCore Dataset, Sensors, 19 (2019), 1553.

15. G. B. Huang, Q. Y. Zhu and C. K. Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, Neural Networks, 2 (2004), 985-990.

16. C. He, F. Xu, Y. Liu, et al., A fast kernel extreme learning machine based on conjugate gradient, Network Comput. Neural Syst., 29 (2018), 70-80.

17. G. B. Huang, X. Ding and H. Zhou, Optimization method based extreme learning machine for classification, Neurocomputing, 74 (2010), 155-163.

18. W. Zou, F. Yao, B. Zhang, et al., Back Propagation Convex Extreme Learning Machine, Proc. ELM, 9 (2016), 259-272.

19. C. He, Y. Liu, T. Yao, et al., A fast learning algorithm based on extreme learning machine for regular fuzzy neural network, J. Intell. Fuzzy Syst., 36 (2019), 3263-3269.

20. W. Deng, Q. Zheng and L. Chen, Regularized Extreme Learning Machine, 2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009, 389-395. Available from: https://ieeexplore.ieee.org/abstract/document/4938676.

21. W. Y. Deng, Q. H. Zheng, L. Chen, et al., Study on extreme learning method of neural network, Chin. J. Comput., 33 (2010), 279-287.

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved