Razavi Far Roozbeh (64 results)
Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
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Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Language: English
Published by Springer International Publishing AG, Cham 2022
- Hardcover
- First Edition
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Hardcover. Condition: new. Hardcover. This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer le…arning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications. This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Hardcover
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Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
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Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
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Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
Seller: Biblios, frankfurt am main, GermanyBiblios
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- Softcover
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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher | This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factor…s of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
- Hardcover
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- Softcover
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Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
Seller: Brook Bookstore, Milano, ItalyBrook Bookstore
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Softcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Softcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Softcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Hardcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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Generative Adversarial Learning : Architectures and Applications
Razavi-far, Roozbeh (EDT); Ruiz-garcia, Ariel (EDT); Palade, Vasile (EDT); Schmidhuber, Juergen (EDT)
Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
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Language: English
Published by Springer Nature Switzerland AG, Cham 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
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Hardcover. Condition: new. Hardcover. This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine l…earning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs theoretical developments and their applications. This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Hardcover
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Federated and Transfer Learning
Razavi-far, Roozbeh (EDT); Wang, Boyu (EDT); Taylor, Matthew E. (EDT); Yang, Qiang (EDT)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Language: English
Published by Springer 2023
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Generative Adversarial Learning : Architectures and Applications
Razavi-far, Roozbeh (EDT); Ruiz-garcia, Ariel (EDT); Palade, Vasile (EDT); Schmidhuber, Juergen (EDT)
Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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Generative Adversarial Learning : Architectures and Applications
Razavi-far, Roozbeh (EDT); Ruiz-garcia, Ariel (EDT); Palade, Vasile (EDT); Schmidhuber, Juergen (EDT)
Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
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- Softcover
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Taschenbuch. Condition: Neu. Federated and Transfer Learning | Roozbeh Razavi-Far (u. a.) | Taschenbuch | Adaptation, Learning, and Optimization | viii | Englisch | 2023 | Springer | EAN 9783031117503 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[…dot]com | Anbieter: preigu.
- Hardcover
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Generative Adversarial Learning : Architectures and Applications
Razavi-far, Roozbeh (EDT); Ruiz-garcia, Ariel (EDT); Palade, Vasile (EDT); Schmidhuber, Juergen (EDT)
Language: English
Published by Springer 2022
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Language: English
Published by Springer 2023
Series: Intelligent Systems Reference Library, Book 182 of 188. Book 182 of 188 - Intelligent Systems Reference Library
- Softcover
Seller: Books Puddle, New York, U.S.A.Books Puddle
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Condition: New. First Edition NO-PA16APR2015-KAP.
- Hardcover
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as we…ll as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
