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Proceedings of ELM 2018 - 9783030233068
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Specificaties
- Objectstaat
- Book Title
- Proceedings of ELM 2018
- ISBN
- 9783030233068
- Publication Name
- Proceedings of Elm 2018
- Publisher
- Springer Nature Switzerland A&G
- Subject
- Computer Science
- Publication Year
- 2019
- Series
- Proceedings in Adaptation, Learning and Optimization
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 235 mm
- Item Weight
- 699 g
- Item Width
- 155 mm
- Number of Pages
- 347 Pages
Over dit product
Product Information
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.
Product Identifiers
Publisher
Springer Nature Switzerland A&G
ISBN-13
9783030233068
eBay Product ID (ePID)
22046648756
Product Key Features
Number of Pages
347 Pages
Language
English
Publication Name
Proceedings of Elm 2018
Publication Year
2019
Subject
Computer Science
Type
Textbook
Series
Proceedings in Adaptation, Learning and Optimization
Format
Hardcover
Dimensions
Item Height
235 mm
Item Weight
699 g
Item Width
155 mm
Volume
11
Additional Product Features
Editor
Jiuwen Cao, Chi Man Vong, Amaury Lendasse, Yoan Miche
Country/Region of Manufacture
Switzerland
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Handelsregistratienummer:
- 06687355
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