9783030131821 - Proceedings of Elm-2017: 10 (proceedings in Adaptation, Learning and Optimization, 10) (13 results)
Language: English
Published by Springer International Publishing 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
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Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
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Proceedings of Elm-2017
Cao, Jiuwen (EDT); Vong, Chi Man (EDT); Miche, Yoan (EDT); Lendasse, Amaury (EDT)
Language: English
Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Proceedings of Elm-2017
Cao, Jiuwen (EDT); Vong, Chi Man (EDT); Miche, Yoan (EDT); Lendasse, Amaury (EDT)
Language: English
Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Taschenbuch. Condition: Neu. Proceedings of ELM-2017 | Jiuwen Cao (u. a.) | Taschenbuch | vii | Englisch | 2019 | Springer | EAN 9783030131821 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Language: English
Published by Springer International Publishing 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4-7, 2017. The book covers theories, algorithms and applications of ELM.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 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.It gives readers a glance of the most recent advances of ELM.
Proceedings of Elm-2017
Cao, Jiuwen (Editor)/ Vong, Chi Man (Editor)/ Miche, Yoan (Editor)/ Lendasse, Amaury (Editor)
Language: English
Published by Springer-Verlag New York Inc 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Paperback. Condition: Brand New. reprint edition. 350 pages. 9.25x6.10x0.73 inches. In Stock.
Language: English
Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Published by Springer 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
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Language: English
Published by Springer International Publishing Dez 2019 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4-7, 2017. The book covers theories, algorithms and applications of ELM.Extreme Learnin…g 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 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.It gives readers a glance of the most recent advances of ELM. 348 pp. Englisch.
Language: English
Published by Springer, Springer Dez 2019 2019
Series: Proceedings in Adaptation, Learning and Optimization, Book 6 of 10. Book 6 of 10 - Proceedings in Adaptation, Learning and Optimization
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4-7, 2017. The book covers theories, algorithms and applications of ELM.Extreme Learning Ma…chines (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 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.It gives readers a glance of the most recent advances of ELM.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 348 pp. Englisch.
