björk kaj mikael (48 Ergebnisse)

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).

Zustand: New. In.

Hardcover. Zustand: 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 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. 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.

Zustand: New. 2024th edition NO-PA16APR2015-KAP.

Taschenbuch. Zustand: Neu. Proceedings of ELM 2022 | Theory, Algorithms and Applications | Kaj-Mikael Björk | Taschenbuch | v | Englisch | 2025 | Springer Nature Switzerland | EAN 9783031550584 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware -This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 88 pp. Englisch.

Buch. Zustand: Neu. Neuware -This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 88 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains selected papers from the 12th International Conference on Extreme Learning Machines 2022. Extreme learning machines (ELMs) continue to be an important complement to the many deep learning models you can find in the machine learning domain. ELM is fast and therefore suitable for many applications (not only in edge computing), and therefore there is a need to gather examples of possible applications. These proceedings, for the ELM 2022 conference, cover several application areas with relevant topics, where ELM can be used and has been used with great success. Here you will find several new areas (gaming, for instance) as well as improved concepts for existing application areas (signature forgery, for instance), where ELM has been implemented. In addition, some method improvements are also covered in this book, more specifically on the topic of 2nd-order Ordinary Differential Equations (ODEs).

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Hardcover. Zustand: Brand New. 86 pages. 9.25x6.10x9.21 inches. In Stock.

Taschenbuch. Zustand: Neu. Proceedings of ELM 2021 | Theory, Algorithms and Applications | Kaj-Mikael Björk | Taschenbuch | viii | Englisch | 2024 | Springer International Publishing | EAN 9783031216800 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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

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

Taschenbuch. Zustand: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.

Buch. Zustand: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.

Taschenbuch. Zustand: 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.

Buch. Zustand: 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.

Hardcover. Zustand: 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 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. 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.

Paperback. Zustand: Brand New. 180 pages. 9.25x6.10x0.41 inches. In Stock.

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

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.