Editors:
- 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
Part of the book series: Proceedings in Adaptation, Learning and Optimization (PALO, volume 10)
Conference series link(s): ELM: International Conference on Extreme Learning Machine
Conference proceedings info: ELM 2017.
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Table of contents (30 papers)
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Front Matter
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.
Editors and Affiliations
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Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
Jiuwen Cao
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Department of Computer and Information Science, University of Macau, Macau, China
Chi Man Vong
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Nokia Bell Labs, Espoo, Finland
Yoan Miche
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Department of Information and Logistics, College of Technology at the University of Houston, Houston, USA
Amaury Lendasse
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
DOI: https://doi.org/10.1007/978-3-030-01520-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-01519-0Published: 17 October 2018
Softcover ISBN: 978-3-030-13182-1Published: 10 December 2019
eBook ISBN: 978-3-030-01520-6Published: 16 October 2018
Series ISSN: 2363-6084
Series E-ISSN: 2363-6092
Edition Number: 1
Number of Pages: VII, 340
Number of Illustrations: 130 b/w illustrations