Librería: Homeless Books, Berlin, Alemania
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Añadir al carritoHardcover. Condición: Sehr gut. 1. Auflage. Unread book in excellent condition. Language - English. Ships from Berlin.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Idioma: Inglés
Publicado por Springer International Publishing, 2018
ISBN 10: 3319861573 ISBN 13: 9783319861579
Librería: moluna, Greven, Alemania
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Publicado por Springer International Publishing, 2017
ISBN 10: 3319574205 ISBN 13: 9783319574202
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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Proceedings of ELM2019 | Jiuwen Cao (u. a.) | Taschenbuch | vi | Englisch | 2021 | Springer | EAN 9783030590499 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Añadir al carritoTaschenbuch. Condición: Neu. Proceedings of ELM-2016 | Jiuwen Cao (u. a.) | Taschenbuch | xiii | Englisch | 2018 | Springer | EAN 9783319861579 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Publicado por Springer International Publishing, 2021
ISBN 10: 3030590496 ISBN 13: 9783030590499
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 149,79
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14-16, 2019. Extreme Learning Machines (ELMs) aim 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 ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.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.
Idioma: Inglés
Publicado por Springer International Publishing, Springer International Publishing, 2018
ISBN 10: 3319861573 ISBN 13: 9783319861579
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December13-15, 2016. 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. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neurosciencesuggests 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. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large-scale computing and artificial intelligence.This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 220,23
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Añadir al carritoPaperback. Condición: Brand New. 188 pages. 9.25x6.10x0.45 inches. In Stock.
Idioma: Inglés
Publicado por Springer International Publishing, 2017
ISBN 10: 3319574205 ISBN 13: 9783319574202
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December13-15, 2016. 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. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neurosciencesuggests 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. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large-scale computing and artificial intelligence.This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 216,60
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Idioma: Inglés
Publicado por Springer International Publishing, 2019
ISBN 10: 3030131823 ISBN 13: 9783030131821
Librería: moluna, Greven, Alemania
EUR 180,07
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Idioma: Inglés
Publicado por Springer International Publishing, 2020
ISBN 10: 3030589889 ISBN 13: 9783030589882
Librería: moluna, Greven, Alemania
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Publicado por Springer International Publishing, 2019
ISBN 10: 3030233065 ISBN 13: 9783030233068
Librería: moluna, Greven, Alemania
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Publicado por Springer International Publishing, 2020
ISBN 10: 303023309X ISBN 13: 9783030233099
Librería: moluna, Greven, Alemania
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Publicado por Springer International Publishing, 2018
ISBN 10: 303001519X ISBN 13: 9783030015190
Librería: moluna, Greven, Alemania
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 224,35
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Idioma: Inglés
Publicado por Springer International Publishing, 2020
ISBN 10: 3030589889 ISBN 13: 9783030589882
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 171,19
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14-16, 2019. Extreme Learning Machines (ELMs) aim 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 ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.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.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 239,39
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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