Proceedings of ELM 2018

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Proceedings of ELM 2018 by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783030233075
Publisher: Springer International Publishing Publication: June 29, 2019
Imprint: Springer Language: English
Author:
ISBN: 9783030233075
Publisher: Springer International Publishing
Publication: June 29, 2019
Imprint: Springer
Language: English

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.

 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

 

More books from Springer International Publishing

Cover of the book Pancreatology by
Cover of the book Women’s Manga in Asia and Beyond by
Cover of the book Was Communism Doomed? by
Cover of the book On the Logos: A Naïve View on Ordinary Reasoning and Fuzzy Logic by
Cover of the book Fisheries, Quota Management and Quota Transfer by
Cover of the book Double-Gyroid-Structured Functional Materials by
Cover of the book Application of FPGA to Real‐Time Machine Learning by
Cover of the book Editors Construct the Renaissance Canon, 1825-1915 by
Cover of the book Prokaryotic Cytoskeletons by
Cover of the book From Particle Systems to Partial Differential Equations III by
Cover of the book A Psychology of User Experience by
Cover of the book Operative Dictations in General and Vascular Surgery by
Cover of the book The Private Sport Sector in Europe by
Cover of the book Contemporary Urban Design Thinking by
Cover of the book The Structure and Stability of Persistence Modules by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy