Proceedings of Elm-2015 Volume 2: Theory, Algorithms and Applications (II), Cao Jiuwen, Mao Kezhi, Wu Jonathan
Автор: Haines Duane E. Название: Fundamental Neuroscience for Basic and Clinical Applications ISBN: 0323396321 ISBN-13(EAN): 9780323396325 Издательство: Elsevier Science Рейтинг: Цена: 12968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Using a rigorous yet clinically-focused approach, Fundamental Neuroscience for Basic and Clinical Applications, 5th Edition, covers the fundamental neuroscience information needed for coursework, exams, and beyond. It integrates neuroanatomy, pharmacology, and physiology, and offers a full section devoted to systems neurobiology, helping you comprehend and retain the complex material you need to know.
Highlights clinical content in blue
throughout the text, helping you focus on what you need to know in the clinical environment.
Presents thoroughly updated information in every chapter, with an emphasis on new clinical thinking as related to the brain and systems neurobiology.
Features hundreds of correlated state-of-the-art imaging examples, anatomical diagrams, and histology photos - nearly half are new or improved for this edition.
Pays special attention to the correct use of clinical and anatomical terminology, and provides new clinical text and clinical-anatomical correlations.
Автор: Jiuwen Cao; Kezhi Mao; Jonathan Wu; Amaury Lendass Название: Proceedings of ELM-2015 Volume 2 ISBN: 3319283723 ISBN-13(EAN): 9783319283722 Издательство: Springer Рейтинг: Цена: 32652.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papersfrom the International Conference on Extreme Learning Machine 2015,which was held in Hangzhou, China,December 15-17,2015.
Автор: Jiuwen Cao; Kezhi Mao; Jonathan Wu; Amaury Lendass Название: Proceedings of ELM-2015 Volume 1 ISBN: 3319283960 ISBN-13(EAN): 9783319283968 Издательство: Springer Рейтинг: Цена: 32652.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papersfrom the International Conference on Extreme Learning Machine 2015,which was held in Hangzhou, China,December 15-17,2015.
Описание: This book gathers selected papers presented at the conference "Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology," one of the first initiatives devoted to the problems of 3D imaging in all contemporary scientific and application areas.
Описание: Dit boek beschrijft de methode CATS CM, versie 4 voor het managen van contracten en is toe te passen binnen private en publieke organisaties, bij opdrachtgevers en leveranciers.Contractmanagement is het realiseren van de met het contract beoogde doelstellingen door tijdens de uitvoeringsfase van een contract, proactief invulling te geven aan alle contractueel vastgelegde verantwoordelijkheden, verplichtingen, procedures, afspraken, voorwaarden en tarieven, het management van de risico`s, het oplossen van alle onduidelijkheden, tegenstrijdigheden en hiaten en het verzorgen van de gewenste wijzigingen in het contract. CATS CM(R) biedt een methodische aanpak voor contractmanagement. Het beschrijft de uitgangspunten, de rollen, de aandachtpunten voor de contractmanager en de te volgen werkwijze. Naast een beschrijving van de methode biedt CATS CM versie 4 ook handvatten voor de implementatie van contractmanagement zowel voor beleid als proces.Een groot aantal organisaties heeft CATS CM inmiddels geselecteerd als basis voor hun contractmanagementproces. Mede op basis van hun ervaringen met CATS CM is deze nieuwe versie 4 ontstaan. CATS CM 4 gaat net als voorgaande edities uit van het standpunt dat het managen van een contract in uitvoering, aan beide kanten van het contract (opdrachtgever en leverancier) sterke overeenkomsten vertoont en dus het best in samenhang wordt beschreven. Dit boek is bedoeld voor iedereen die verantwoordelijk is voor, dan wel te maken heeft met contracten in uitvoering: contractmanagers, businessmanagers, deliverymanagers, projectmanagers, servicemanagers, facilitymanagers, inkopers, inkoopmanagers, compliance managers, risicomanagers, accountmanagers, salesmanagers en HR-managers, hun directeuren en stuurgroepleden aan beide zijden van het contract.
Автор: Jiuwen Cao; Erik Cambria; Amaury Lendasse; Yoan Mi Название: Proceedings of ELM-2016 ISBN: 3319574205 ISBN-13(EAN): 9783319574202 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 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 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. 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.
Автор: Jiuwen Cao; Kezhi Mao; Erik Cambria; Zhihong Man; Название: Proceedings of ELM-2014 Volume 1 ISBN: 331936684X ISBN-13(EAN): 9783319366845 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014.
Автор: Jiuwen Cao; Kezhi Mao; Erik Cambria; Zhihong Man; Название: Proceedings of ELM-2014 Volume 2 ISBN: 3319366858 ISBN-13(EAN): 9783319366852 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014.
Автор: Jiuwen Cao; Erik Cambria; Amaury Lendasse; Yoan Mi Название: Proceedings of ELM-2016 ISBN: 3319861573 ISBN-13(EAN): 9783319861579 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 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 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. 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.
Автор: Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lenda Название: Proceedings of ELM-2017 ISBN: 3030131823 ISBN-13(EAN): 9783030131821 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4–7, 2017. The book covers theories, algorithms and applications of ELM.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. 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. It gives readers a glance of the most recent advances of ELM.
Автор: Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lenda Название: Proceedings of ELM 2018 ISBN: 3030233065 ISBN-13(EAN): 9783030233068 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lenda Название: Proceedings of ELM-2017 ISBN: 303001519X ISBN-13(EAN): 9783030015190 Издательство: Springer Рейтинг: Цена: 30745.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4–7, 2017. The book covers theories, algorithms and applications of ELM.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. 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. It gives readers a glance of the most recent advances of ELM.
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