Information Theoretic Principles for Agent Learning



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Springer


Paru le : 2024-08-05



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Description

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.
Pages
95 pages
Collection
n.c
Parution
2024-08-05
Marque
Springer
EAN papier
9783031653872
EAN PDF
9783031653889

Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
2560 Ko
Prix
52,74 €
EAN EPUB
9783031653889

Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
5120 Ko
Prix
52,74 €

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.

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