Proceedings of ELM-2017

  • Jiuwen Cao
  • Chi Man Vong
  • Yoan Miche
  • Amaury Lendasse
Conference proceedings ELM 2017

Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 10)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Caitong Yue, Jing Liang, Boyang Qu, Zhuopei Lu, Baolei Li, Yuhong Han
    Pages 12-21
  3. Yanhui Li, Ye Yuan, Guoren Wang
    Pages 22-34
  4. Xiaoyi Yang, Xinli Deng, Lei Shi
    Pages 35-45
  5. Kun Qin, Lei Sun, Shengmin Zhou, Badong Chen, Beom-Seok Oh, Zhiping Lin
    Pages 46-57
  6. Sicheng Yu, Xibei Yang, Xiangjian Chen, Pingxin Wang
    Pages 70-79
  7. Shu-li Jia, Chong Qu, Wenjing Lin, Shuhao Cai, Liyong Ma
    Pages 103-113
  8. Jinwei Hu, Yuan Lan, Xianghui Zeng, Jiahai Huang, Bing Wu, Liwei Yao et al.
    Pages 114-122
  9. Lu Zhang, Hong Cheng, Huanghuang Liang, Yang Zhao, Xinqiang Pan, Yuansheng Luo et al.
    Pages 123-134
  10. Zhang Jing, Ren Yonggong
    Pages 150-161
  11. Ulas Baran Baloglu, Ozal Yildirim, Ayşegül Uçar
    Pages 162-171
  12. Xingyu Zhao, Shifei Ding, Yuexuan An
    Pages 172-180
  13. Anton Akusok, Emil Eirola, Kaj-Mikael Björk, Amaury Lendasse
    Pages 181-185
  14. Dandan Zhang, Yuanlong Yu, Zhiyong Huang
    Pages 186-202
  15. Emil Eirola, Anton Akusok, Kaj-Mikael Björk, Amaury Lendasse
    Pages 203-209
  16. Hanman Li, Lidan Wang, ShuKai Duan
    Pages 210-218
  17. Yanan Liu, Sen Zhang, Yixin Yin, Xiaoli Su, Jie Dong
    Pages 219-229
  18. Tan Guo, Xiaoheng Tan, Lei Zhang
    Pages 230-239
  19. Anton Akusok, Mirka Saarela, Tommi Kärkkäinen, Kaj-Mikael Björk, Amaury Lendasse
    Pages 240-248
  20. Xiangyang Deng, Zhenyu Li, Dongshun Cui, Gaoming Huang, Jiawen Feng, Liming Zhang
    Pages 249-261
  21. Zhan-Li Sun, Nan Wang, Ru-Xia Ban, Xia Chen
    Pages 262-270
  22. Song Cui, Lijuan Duan, Yuanhua Qiao, Xing Su
    Pages 271-281
  23. Shu-Heng Ma, Zhan-Li Sun, Cheng-Gang Gu
    Pages 282-291
  24. Dongshun Cui, Kai Hu, Guanghao Zhang, Wei Han, Guang-Bin Huang
    Pages 292-303
  25. Andrey Gritsenko, Zhiyu Sun, Stephen Baek, Yoan Miche, Renjie Hu, Amaury Lendasse
    Pages 304-316
  26. Linyuan Yu, Yan Liu, Wentao Zhao, Qiang Liu, Jiaohua Qin
    Pages 317-326
  27. Back Matter
    Pages 339-340

About these proceedings


This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4–7, 2017. The book covers theories, algorithms and applications of ELM.


Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers.


This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning.


It gives readers a glance of the most recent advances of ELM.



Intelligent Systems Extreme Learning Machines Multiagent Systems ELM 2017 The International Conference on Extreme Learning Machines

Editors and affiliations

  • Jiuwen Cao
    • 1
  • Chi Man Vong
    • 2
  • Yoan Miche
    • 3
  • Amaury Lendasse
    • 4
  1. 1.Institute of Information and ControlHangzhou Dianzi UniversityZhejiangChina
  2. 2.Department of Computer and Information ScienceUniversity of MacauMacauChina
  3. 3.Nokia Bell LabsEspooFinland
  4. 4.Department of Information and LogisticsCollege of Technology at the University of HoustonHoustonUSA

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