Isbn: 9783031216770 - proceedings of elm 2021: theory, algorithms and applications: 16 (13 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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  • Lingua: Inglese

    Editore: Springer, 2023

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  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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    Condizione: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.

  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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    Hardcover. Condizione: Brand New. 180 pages. 9.25x6.10x0.44 inches. In Stock.

  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 dataand 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.

  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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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, 2023

    3031216776 / 9783031216770

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    Da: moluna, Greven, Germaniamoluna

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    Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides recent research on Extreme Learning Machines (ELM)Contains selected papers from the 11th International Conference on Extreme Learning Machines 2022Presents theory, algorithms, and applications of ELMThis book contains pa.

  • Lingua: Inglese

    Editore: Springer International Publishing Jan 2023, 2023

    3031216776 / 9783031216770

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    EUR 235,39

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    Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 dataand 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. 180 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer, Springer International Publishing Jan 2023, 2023

    3031216776 / 9783031216770

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    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 dataand 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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  • Lingua: Inglese

    Editore: Springer, 2023

    3031216776 / 9783031216770

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    Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios

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    Condizione: New. PRINT ON DEMAND.