9781108474443-1108474446-Analytic Information Theory: From Compression to Learning

Analytic Information Theory: From Compression to Learning

ISBN-13: 9781108474443
ISBN-10: 1108474446
Edition: 1
Author: Wojciech Szpankowski, Michael Drmota
Publication date: 2023
Publisher: Cambridge University Press
Format: Hardcover 400 pages
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Book details

ISBN-13: 9781108474443
ISBN-10: 1108474446
Edition: 1
Author: Wojciech Szpankowski, Michael Drmota
Publication date: 2023
Publisher: Cambridge University Press
Format: Hardcover 400 pages

Summary

Analytic Information Theory: From Compression to Learning (ISBN-13: 9781108474443 and ISBN-10: 1108474446), written by authors Wojciech Szpankowski, Michael Drmota, was published by Cambridge University Press in 2023. With an overall rating of 4.1 stars, it's a notable title among other books. You can easily purchase or rent Analytic Information Theory: From Compression to Learning (Hardcover) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

Through information theory, problems of communication and compression can be precisely modeled, formulated, and analyzed, and this information can be transformed by means of algorithms. Also, learning can be viewed as compression with side information. Aimed at students and researchers, this book addresses data compression and redundancy within existing methods and central topics in theoretical data compression, demonstrating how to use tools from analytic combinatorics to discover and analyze precise behavior of source codes. It shows that to present better learnable or extractable information in its shortest description, one must understand what the information is, and then algorithmically extract it in its most compact form via an efficient compression algorithm. Part I covers fixed-to-variable codes such as Shannon and Huffman codes, variable-to-fixed codes such as Tunstall and Khodak codes, and variable-to-variable Khodak codes for known sources. Part II discusses universal source coding for memoryless, Markov, and renewal sources.

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