Items related to Analytic Information Theory: From Compression to Learning

Analytic Information Theory: From Compression to Learning - Hardcover

Drmota, Michael; Szpankowski, Wojciech

 
9781108474443: Analytic Information Theory: From Compression to Learning
Hardcover
ISBN 10: 1108474446 ISBN 13: 9781108474443
Publisher: Cambridge University Press, 2023

Synopsis

Explores problems of information and learning theory, using tools from analytic combinatorics to analyze precise behavior of source codes.

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About the Authors

Michael Drmota is Professor for Discrete Mathematics at TU Wien. His research activities range from analytic combinatorics over discrete random structures to number theory. He has published several books, including 'Random Trees' (2009), and about 200 research articles. He was President of the Austrian Mathematical Society from 2010 to 2013, and has been Corresponding Member of the Austrian Academy of Sciences since 2013.

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University where he teaches and conducts research in analysis of algorithms, information theory, analytic combinatorics, random structures, and machine learning for classical and quantum data. He has received the Inaugural Arden L. Bement Jr. Award (2015) and the Flajolet Lecture Prize (2020), among others. In 2021, he was elected to the Academia Europaea. In 2008, he launched the interdisciplinary Institute for Science of Information, and in 2010, he became the Director of the NSF Science and Technology Center for Science of Information.

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Publisher
Cambridge University Press
Publication date
2023
Language
English
ISBN 10 
1108474446
ISBN 13 
9781108474443
Binding
Hardcover
Number of pages
380