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© 2018

Proceedings of ELM-2016

  • Jiuwen Cao
  • Erik Cambria
  • Amaury Lendasse
  • Yoan Miche
  • Chi Man Vong
Conference proceedings

Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 9)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Francesco Grasso, Antonio Luchetta, Stefano Manetti
    Pages 11-25
  3. Hui Lv, Huisheng Zhang
    Pages 27-36
  4. Peng Bai, Huaping Liu, Fuchun Sun, Meng Gao
    Pages 37-51
  5. Hongjie Geng, Huaping Liu, Bowen Wang, Fuchun Sun
    Pages 61-73
  6. Muhammad Rizwan, Abdul Hafeez, Ali R. Butt, Samir M. Iqbal
    Pages 75-87
  7. Xu Jingting, Feng Jun, Sun Xia, Zhang Lei, Liu Xiaoning
    Pages 89-97
  8. Hongbo Wang, Peng Song, Chengyao Wang, Xuyan Tu
    Pages 113-127
  9. Aapo Kalliola, Yoan Miche, Ian Oliver, Silke Holtmanns, Buse Atli, Amaury Lendasse et al.
    Pages 129-143
  10. Xiaodong Li, Weijie Mao, Wei Jiang, Ye Yao
    Pages 159-170
  11. Anton Akusok, Emil Eirola, Yoan Miche, Ian Oliver, Kaj-Mikael Björk, Andrey Gritsenko et al.
    Pages 183-193
  12. Emil Eirola, Anton Akusok, Kaj-Mikael Björk, Hans Johnson, Amaury Lendasse
    Pages 195-206
  13. Lei Cai, Jianqing Zhu, Huanqiang Zeng, Jing Chen, Canhui Cai
    Pages 207-215
  14. Peng Bian, Yi Jin, Jiuwen Cao
    Pages 217-228

About these proceedings

Introduction

This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. 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.  Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), 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. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence.

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

Keywords

Intelligent Systems Extreme Learning Machines Multiagent Systems ELM 2016 The International Conference on Extreme Learning Machines

Editors and affiliations

  • Jiuwen Cao
    • 1
  • Erik Cambria
    • 2
  • Amaury Lendasse
    • 3
  • Yoan Miche
    • 4
  • Chi Man Vong
    • 5
  1. 1.Institute of Information and ControlHangzhou Dianzi UniversityZhejiangChina
  2. 2.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Mechanical and Industrial EngineeringUniversity of IowaIowa CityUSA
  4. 4.Department of Information and Computer Science, School of ScienceAalto UniversityAaltoFinland
  5. 5.Department of Computer and Information ScienceUniversity of MacauMacauChina

Bibliographic information

  • Book Title Proceedings of ELM-2016
  • Editors Jiuwen Cao
    Erik Cambria
    Amaury Lendasse
    Yoan Miche
    Chi Man Vong
  • Series Title Proceedings in Adaptation, Learning and Optimization
  • Series Abbreviated Title Proceedings in Adaptation, Learning and Optimization
  • DOI https://doi.org/10.1007/978-3-319-57421-9
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-57420-2
  • Softcover ISBN 978-3-319-86157-9
  • eBook ISBN 978-3-319-57421-9
  • Series ISSN 2363-6084
  • Series E-ISSN 2363-6092
  • Edition Number 1
  • Number of Pages XIII, 285
  • Number of Illustrations 17 b/w illustrations, 126 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
  • Buy this book on publisher's site