9783031216770 - proceedings of elm 2021: theory, algorithms and applications: 16 (proceedings in adaptation, learning and optimization, 16) (16 resultados)
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Hardcover. Condición: new. Hardcover. This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 1516, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see th…e 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Buch. Condición: 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.
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Condición: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
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Hardcover. Condición: new. Hardcover. This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 1516, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see th…e 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Condición: new. Questo è un articolo print on demand.
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Hardcover. Condición: Brand New. 180 pages. 9.25x6.10x0.44 inches. In Stock. This item is printed on demand.
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Condición: 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.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: 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.
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Idioma: Inglés
Editorial: Springer, Springer International Publishing Jan 2023, 2023
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Buch. Condición: 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 a…dvocated 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.
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