9783030233099 - proceedings of elm 2018: 11 (14 risultati)
Lingua: Inglese
Editore: Springer Nature Switzerland AG, Cham, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Paperback. Condizione: new. Paperback. This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 2123, 2018. This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical st…udies 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: Springer International Publishing, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Condizione: New.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Condizione: New. In.
Altre immaginiLingua: Inglese
Editore: Palgrave Macmillan, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Taschenbuch. Condizione: Neu. Proceedings of ELM 2018 | Jiuwen Cao (u. a.) | Taschenbuch | Proceedings in Adaptation, Learning and Optimization | viii | Englisch | 2020 | Palgrave Macmillan | EAN 9783030233099 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]…springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. pp. 347 1st ed. 2020 edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer International Publishing, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 e…xchange 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.
Proceedings of Elm 2018
Cao, Jiuwen (Editor)/ Vong, Chi Man (Editor)/ Miche, Yoan (Editor)/ Lendasse, Amaury (Editor)
Lingua: Inglese
Editore: Springer Nature, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 356 pages. 9.25x6.10x0.74 inches. In Stock.
Lingua: Inglese
Editore: Springer Nature Switzerland AG, Cham, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Paperback. Condizione: new. Paperback. This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 2123, 2018. This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical st…udies 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Aug 2020, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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 2018, which was held in Singapore, November 21-23, 2018. This conference provided a forum for academics, researchers and engineer…s 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. 356 pp. Englisch.
Lingua: Inglese
Editore: Springer, Springer Aug 2020, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Taschenbuch. Condizione: 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 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 356 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand pp. 347.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 7 di 10 - Proceedings in Adaptation, Learning and Optimization
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND pp. 347.
