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Proceedings of ELM 2021theory, algorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Proceedings of ELM 2021edited by Kaj-Mikael Björk.
Reminder of title:	theory, algorithms and applications /
remainder title:	ELM 2021
other author:	Björk, Kaj-Mikael.
corporate name:
Published:	Cham :Springer International Publishing :2023.
Description:	viii, 172 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence
Online resource:	https://doi.org/10.1007/978-3-031-21678-7
ISBN:	9783031216787$q(electronic bk.)

Proceedings of ELM 2021theory, algorithms and applications /
Proceedings of ELM 2021theory, algorithms and applications /[electronic resource] :ELM 2021edited by Kaj-Mikael Björk. - Cham :Springer International Publishing :2023. - viii, 172 p. :ill. (some col.), digital ;24 cm. - Proceedings in adaptation, learning and optimization,v. 162363-6092 ;. - Proceedings in adaptation, learning and optimization ;v.2..

Pretrained E-commerce Knowledge Graph Model for Product Classification -- A Novel Methodology for Object Detection in Highly Cluttered Images -- Extreme learning Machines for Offline Forged Signature Identification -- Randomized model structure selection approach for Extreme Learning Machine applied to Acid sulfate soils detection -- Online label distribution learning based on kernel extreme learning machine.

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 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. 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.

ISBN: 9783031216787$q(electronic bk.)

Standard No.: 10.1007/978-3-031-21678-7doiSubjects--Topical Terms:

252958
Artificial intelligence

LC Class. No.: Q334

Dewey Class. No.: 006.3

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520 $a 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 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. 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.
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