Proceedings of ELM-2017, Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lenda
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.
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; 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 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.
Описание: This book gathers the proceedings of the 3rd International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI2017), which took place in Cairo, Egypt from September 9 to 11, 2017.
A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal.- Neuro-Fuzzy System for Medium-term Electric Energy Demand Forecasting.- Multivariate Regression Tree for Pattern-based Forecasting Time Series with Multiple Seasonal Cycles.- PID-fuzzy Controllers with Dynamic Structure and Evolutionary Method for Their Construction.- How Is Server Software Configured? Examining the Structure of Configuration Files.- Frequent Scene Change in Wavelet Video Compression.- A Versatile Hardware and Software Toolset for Computer Aided Inspection Planning of Machine Vision Applications
Описание: This first edition of conference Proceedings reflects the expansion of the field of Mechatronics, which has now taken its place in the world of newer transdisciplinary fields of Adaptronics, Integronics, and Cyber-Mix Mechatronics. It presents state-of-the art advances in Mechatronics, Adaptronics, Integronics and Cyber-Mix-Mechatronics. The 1st International Conference of Mechatronics and Cyber-MixMechatronics/ICOMECYME was organized by the National Institute of R&D in Mechatronics and Measurement Technique in Bucharest (Romania), on September 7th–8th, 2017 and attracted specialists from all over the world—including North America, South America, and Asia. In addition to presenting research results, ICOMECYME also offered a forum for exchange between R&D experts.
Описание: This book provides an overview of current research in the fascinating, interdisciplinary field of computer science and sports. It includes papers from the 11th International Symposium on Computer Science in Sport (IACSS 2017), which took place in Constance, Germany, on September 6–9, 2017. The papers represent the state of the art in utilizing the latest developments in computer science to support coaches and athletes. The book covers a broad range of topics, reflecting the diversity of the field. It presents three categories of papers: those on concepts in informatics like modeling, virtual reality, simulation; those describing applications of computer science in sports like running, volleyball, water polo, and football; and contributions discussing the impact of computer science in sports federations and universities.
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