Proceedings of ELM 2018

  • Jiuwen Cao
  • Chi Man Vong
  • Yoan Miche
  • Amaury Lendasse
Conference proceedings ELM 2018

Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 11)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Weipeng Cao, Jinzhu Gao, Xizhao Wang, Zhong Ming, Shubin Cai
    Pages 1-10
  3. Yanbing Chen, Tao Liu, Jianjun Chen, Dongqi Li, Mengya Wu
    Pages 11-16
  4. Lingkai Xing, Zhihong Man, Jinchuan Zheng, Tony Cricenti, Mengqiu Tao
    Pages 27-36
  5. Zhongyang Wang, Junchang Xin, Yue Zhao, Qiyong Guo
    Pages 37-44
  6. Feixiang Zhao, Mingzhe Liu, Binyang Jia, Xin Jiang, Jun Ren
    Pages 45-54
  7. Zhihong Miao, Qing He
    Pages 55-64
  8. Tan Guo, Lei Zhang, Xiaoheng Tan
    Pages 65-75
  9. Dayu Jia, Junchang Xin, Zhiqiong Wang, Wei Guo, Guoren Wang
    Pages 76-85
  10. Donghong Han, Fulin Wei, Lin Bai, Xiang Tang, TingShao Zhu, Guoren Wang
    Pages 86-97
  11. Xin Sun, K. V. Ling, K. K. Sin, Lawrence Tay
    Pages 98-107
  12. Weiqing Yan, Shuigen Wang, Guanghui Yue, Jindong Xu, Xiangrong Tong, Laihua Wang
    Pages 118-124
  13. Zhen Zhang, Guoren Wang, Xiangguo Zhao
    Pages 125-133
  14. Lu Fang, Huaping Liu, Yanzhi Dong
    Pages 134-143
  15. Liang Li, Guoren Wang, Gang Wu, Qi Zhang
    Pages 144-153
  16. Boyang Li, Guoren Wang, Yurong Cheng, Yongjiao Sun
    Pages 154-162
  17. Haigang Zhang, Jinfeng Yang, Guimin Jia, Shaocheng Han
    Pages 173-181
  18. Hangxu Ji, Gang Wu, Guoren Wang
    Pages 182-190
  19. Nan Liu, Lian Leng Low, Sean Shao Wei Lam, Julian Thumboo, Marcus Eng Hock Ong
    Pages 191-196
  20. Yuliang Ma, Ye Yuan, Guoren Wang, Xin Bi, Zhongqing Wang, Yishu Wang
    Pages 197-206
  21. Sung-Woo Byun, Da-Kyeong Oh, MyoungJin Son, Ju Hee Kim, Ye Jin Lee, Seok-Pil Lee
    Pages 207-215
  22. Lingyun Xiang, Guohan Zhao, Qian Li, Zijie Zhu
    Pages 236-246
  23. Kun Zhang, Lu-Lu Tang, Zhi-Xin Yang, Lu-Qing Luo
    Pages 247-252
  24. Anurag Daram, Karan Paluru, Vedant Karia, Dhireesha Kudithipudi
    Pages 253-262
  25. Zhen Li, Karl Ratner, Edward Ratner, Kallin Khan, Kaj-Mikael Bjork, Amaury Lendasse
    Pages 283-291
  26. Xue He, Tiancheng Zhang, Hengyu Liu, Ge Yu
    Pages 292-302
  27. Guanghao Zhang, Dongshun Cui, Shangbo Mao, Guang-Bin Huang
    Pages 319-327
  28. Yuzhong Peng, Huasheng Zhao, Jie Li, Xiao Qin, Jianping Liao, Zhiping Liu
    Pages 328-335
  29. Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
    Pages 336-344
  30. Back Matter
    Pages 345-347

About these proceedings


This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21–23, 2018. This conference provided 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.

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. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.

This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM.



Intelligent Systems Extreme Learning Machines ELM 2018 The International Conference on Extreme Learning Machines Pervasive Learning Brain Learning Machine Learning

Editors and affiliations

  • Jiuwen Cao
    • 1
  • Chi Man Vong
    • 2
  • Yoan Miche
    • 3
  • Amaury Lendasse
    • 4
  1. 1.Institute of Information and ControlHangzhou Dianzi UniversityXiasha, HangzhouChina
  2. 2.Department of Computer and Information ScienceUniversity of MacauTaipaMacao
  3. 3.Nokia Bell LabsCybersecurity ResearchEspooFinland
  4. 4.Department of Information and Logistics TechnologyUniversity of HoustonHoustonUSA

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