Overview
- Provides recent research on Extreme Learning Machines (ELM)
- Contains selected papers from the International Conference on Extreme Learning Machines 2018, which was held in Singapore, November 21–23, 2018
- Presents theory, algorithms, and applications of ELM
Part of the book series: Proceedings in Adaptation, Learning and Optimization (PALO, volume 11)
Included in the following conference series:
Conference proceedings info: ELM 2018.
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Table of contents (36 papers)
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Front Matter
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Proceedings of ELM 2018
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Proceedings of ELM 2018
Editors: Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Series Title: Proceedings in Adaptation, Learning and Optimization
DOI: https://doi.org/10.1007/978-3-030-23307-5
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 2020
Hardcover ISBN: 978-3-030-23306-8Published: 30 June 2019
Softcover ISBN: 978-3-030-23309-9Published: 15 August 2020
eBook ISBN: 978-3-030-23307-5Published: 29 June 2019
Series ISSN: 2363-6084
Series E-ISSN: 2363-6092
Edition Number: 1
Number of Pages: VIII, 347
Number of Illustrations: 30 b/w illustrations, 79 illustrations in colour