Federated and Transfer Learning: 27 (Adaptation, Learning, and Optimization) - Tapa dura

 
9783031117473: Federated and Transfer Learning: 27 (Adaptation, Learning, and Optimization)
Tapa dura
ISBN 10: 3031117476 ISBN 13: 9783031117473
Editorial: Springer, 2022

Sinopsis

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. 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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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. 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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2022
Idioma
Inglés
ISBN 10 
3031117476
ISBN 13 
9783031117473
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
380
Editor
Wang Boyu, Taylor Matthew E., Razavi-Far Roozbeh
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9783031117503: Federated and Transfer Learning: 27 (Adaptation, Learning, and Optimization)

Edición Destacada

ISBN 10:  3031117506 ISBN 13:  9783031117503
Editorial: Springer, 2023
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