UID:
almahu_9949588295802882
Format:
1 online resource (xvi, 363 pages) :
,
digital, PDF file(s).
ISBN:
9781108565462 (ebook)
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 30 Aug 2023).
,
Preliminaries -- Shannon and Huffman FV codes -- Tunstall and Khodak VF codes -- Divide-and-conquer VF codes -- Khodak VV codes -- Nonprefix one-to-one codes -- Advanced data structures : tree compression -- Graph and structure compression -- Minimax redundancy and regret -- Redundancy of universal memoryless sources -- Markov types and redundancy for Markov sources -- Non-Markovian sources : redundancy of renewal processes.
Additional Edition:
Print version: ISBN 9781108474443
Language:
English
URL:
https://doi.org/10.1017/9781108565462
URL:
Volltext
(URL des Erstveröffentlichers)
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