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Proceedings in Adaptation, Learning and Optimization

Proceedings of ELM-2017

Editors: Cao, J., Vong, C.M., Miche, Y., Lendasse, A. (Eds.)

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  • Provides recent research on Extreme Learning Machine
  •  
  • Includes selected papers from the International Conference on Extreme Learning Machine 2017, which was held in Yantai, China, October 4–7, 2017
  •  
  • Presents Theory, Algorithms and Applications
  •  
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Buy this book

eBook $219.00
price for USA in USD
  • ISBN 978-3-030-01520-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $279.99
price for USA in USD
Softcover $279.99
price for USA in USD
About this book

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.

 

Table of contents (30 chapters)

Table of contents (30 chapters)
  • Adaptive Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine - A Pilot Study

    Pages 1-11

    Wong, Pak Kin (et al.)

  • Sparse Representation Feature for Facial Expression Recognition

    Pages 12-21

    Yue, Caitong (et al.)

  • Protecting User Privacy in Mobile Environment Using ELM-UPP

    Pages 22-34

    Li, Yanhui (et al.)

  • Application Study of Extreme Learning Machine in Image Edge Extraction

    Pages 35-45

    Yang, Xiaoyi (et al.)

  • A Normalized Mutual Information Estimator Compensating Variance Fluctuations for Motion Detection

    Pages 46-57

    Qin, Kun (et al.)

Buy this book

eBook $219.00
price for USA in USD
  • ISBN 978-3-030-01520-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $279.99
price for USA in USD
Softcover $279.99
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Proceedings of ELM-2017
Editors
  • Jiuwen Cao
  • Chi Man Vong
  • Yoan Miche
  • Amaury Lendasse
Series Title
Proceedings in Adaptation, Learning and Optimization
Series Volume
10
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-01520-6
DOI
10.1007/978-3-030-01520-6
Hardcover ISBN
978-3-030-01519-0
Softcover ISBN
978-3-030-13182-1
Series ISSN
2363-6084
Edition Number
1
Number of Pages
VII, 340
Number of Illustrations
130 b/w illustrations
Topics