Analytic Information Theory: From Compression to Learning - Rilegato

Drmota, Michael; Szpankowski, Wojciech

Rilegato

ISBN 10: 1108474446 ISBN 13: 9781108474443

Casa editrice: Cambridge University Press, 2023

Riassunto

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

Su questo libro

Informazioni sugli autori

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.

Dettagli dell'edizione

• Editore: Cambridge University Press
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1108474446
• ISBN 13: 9781108474443
• Rilegatura: Copertina rigida
• Numero di pagine: 380
• Contatto del produttore: non disponibile
• Persona responsabile: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; Francia