Overview
- Provides recent research on Extreme Learning Machines (ELM)
- Contains selected papers from the 11th International Conference on Extreme Learning Machines 2022
- Presents theory, algorithms, and applications of ELM
Part of the book series: Proceedings in Adaptation, Learning and Optimization (PALO, volume 16)
Included in the following conference series:
Conference proceedings info: ELM 2021.
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Table of contents (16 papers)
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Front Matter
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Back Matter
Other volumes
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Proceedings of ELM 2021
Keywords
- Intelligent Systems
- Extreme Learning Machine
- ELM 2021
- The International Conference on Extreme Learning Machines
- Robustness and Stability Analysis
- Real-time Learning/Reasoning
- Clustering and Feature Extraction/Selection
- Multi Hidden Layers Solutions and Random Networks
- Parallel and Distributed Computing / Cloud Computing
- Time Series Predictions
- Pattern Recognition
- Data Analytics
- Super/Ultra-large Scale Data Processing
About this book
This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 15–16, 2021. 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 dataand 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 provides 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.This book covers theories, algorithms, and applications of ELM. It gives readers a glance of the most recent advances of ELM.
Editors and Affiliations
Bibliographic Information
Book Title: Proceedings of ELM 2021
Book Subtitle: Theory, Algorithms and Applications
Editors: Kaj-Mikael Björk
Series Title: Proceedings in Adaptation, Learning and Optimization
DOI: https://doi.org/10.1007/978-3-031-21678-7
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 2023
Hardcover ISBN: 978-3-031-21677-0Published: 19 January 2023
Softcover ISBN: 978-3-031-21680-0Published: 20 January 2024
eBook ISBN: 978-3-031-21678-7Published: 18 January 2023
Series ISSN: 2363-6084
Series E-ISSN: 2363-6092
Edition Number: 1
Number of Pages: VIII, 172
Number of Illustrations: 10 b/w illustrations, 47 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Machine Learning