Editors:
- Recent research on Extreme Learning Machine
- Selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016
- Presents Theory, Algorithms and Applications
Part of the book series: Proceedings in Adaptation, Learning and Optimization (PALO, volume 9)
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Table of contents (22 papers)
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Front Matter
About this book
This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December13-15, 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 neurosciencesuggests 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.
Editors and Affiliations
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Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
Jiuwen Cao
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School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Erik Cambria
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Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, USA
Amaury Lendasse
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Department of Information and Computer Science, School of Science, Aalto University, Aalto, Finland
Yoan Miche
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Department of Computer and Information Science, University of Macau, Macau, China
Chi Man Vong
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
DOI: https://doi.org/10.1007/978-3-319-57421-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-57420-2Published: 26 May 2017
Softcover ISBN: 978-3-319-86157-9Published: 09 May 2018
eBook ISBN: 978-3-319-57421-9Published: 25 May 2017
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
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