Information Theoretic Principles for Agent Learning - Rilegato

Gibson, Jerry D.

Rilegato

ISBN 10: 3031653874 ISBN 13: 9783031653872

Casa editrice: Springer Nature, 2024

Riassunto

This book provides readers with the fundamentals of information theoretic techniques for statistical data science analyses and for characterizing the behavior and performance of a learning agent outside of the standard results on communications and compression fundamental limits. Readers will benefit from the presentation of information theoretic quantities, definitions, and results that provide or could provide insights into data science and learning.

Su questo libro

Informazioni sull'autore

Jerry D. Gibson is Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. He has been an Associate Editor of the IEEE Transactions on Communications and the IEEE Transactions on Information Theory. He was an IEEE Communications Society Distinguished Lecturer for 2007-2008. He is an IEEE Fellow, and he has received The Fredrick Emmons Terman Award (1990), the 1993 IEEE Signal Processing Society Senior Paper Award, the 2009 IEEE Technical Committee on Wireless Communications Recognition Award, and the 2010 Best Paper Award from the IEEE Transactions on Multimedia. He is the author, coauthor, and editor of several books, the most recent of which are The Mobile Communications Handbook (Editor, 3rd ed., 2012), Rate Distortion Bounds for Voice and Video (Coauthor with Jing Hu, NOW Publishers, 2014), and Information Theory and Rate Distortion Theory for Communications and Compression (Morgan-Claypool, 2014). His research interests are lossy source coding, wireless communications and networks, and digital signal processing.

Dalla quarta di copertina

This book provides readers with the fundamentals of information theoretic techniques for statistical data science analyses and for characterizing the behavior and performance of a learning agent outside of the standard results on communications and compression fundamental limits. Readers will benefit from the presentation of information theoretic quantities, definitions, and results that provide or could provide insights into data science and learning.

In addition, this book:

• Describes the fundamentals of information theoretic techniques for statistical data science analyses
• Provides succinct introductions to key topics, with references as needed for further technical depth
• Enables readers from varying backgrounds to understand the behavior and performance of a learning agent

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031653874
• ISBN 13: 9783031653872
• Rilegatura: Copertina rigida
• Numero di pagine: 150
• Contatto del produttore: GPSR Kontakt; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Germania