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Analytic information theory :from compression to learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Analytic information theory :Michael Drmota, Wojciech Szpankowski.
其他題名:	from compression to learning /
作者:	Drmota, Michael,
其他作者:	Szpankowski, Wojciech,
出版者:	Cambridge, United Kingdom ;Cambridge University Press,2023.
面頁冊數:	xvi, 363 p. :ill. ;26 cm.
標題:	Information theory.
ISBN:	9781108474443 :

Analytic information theory :from compression to learning /
Drmota, Michael,1957-

Analytic information theory :from compression to learning /Michael Drmota, Wojciech Szpankowski. - Cambridge, United Kingdom ;Cambridge University Press,2023. - xvi, 363 p. :ill. ;26 cm.

Includes bibliographical references (p. 347-359) and index.

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.

"Aimed at graduate students and researchers interested in information theory and the analysis of algorithms, this book explores problems of information and learning theory, demonstrating how to use tools from analytic combinatorics to discover and analyze precise behavior of source codes"--

ISBN: 9781108474443 :NT$4338

LCCN: 2023026303

Nat. Bib. No.: GBC3E2274bnb

Nat. Bib. Agency Control No.: 021156444UkSubjects--Topical Terms:

183013
Information theory.

LC Class. No.: Q360 / .D76 2023

Dewey Class. No.: 003/.54

Analytic information theory :from compression to learning /
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020 $a 9781108464642 (pbk.)
020 $z 9781108565462 (ebook)
035 $a (OCoLC)1372277549
035 $a on1372277549
040 $a LBSOR $b eng $c DLC $d UKMGB $d YDX $d OCLCF $d OCLCO $d OCLCQ $d YDX
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050 0 0 $a Q360 $b .D76 2023
082 0 0 $a 003/.54 $2 23
100 1 $a Drmota, Michael, $d 1957- $3 544885
245 1 0 $a Analytic information theory : $b from compression to learning / $c Michael Drmota, Wojciech Szpankowski.
260 $a Cambridge, United Kingdom ; $a New York, NY : $b Cambridge University Press, $c 2023.
300 $a xvi, 363 p. : $b ill. ; $c 26 cm.
504 $a Includes bibliographical references (p. 347-359) and index.
505 0 $a 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.
520 $a "Aimed at graduate students and researchers interested in information theory and the analysis of algorithms, this book explores problems of information and learning theory, demonstrating how to use tools from analytic combinatorics to discover and analyze precise behavior of source codes"-- $c Provided by publisher.
650 0 $a Information theory. $3 183013
650 0 $a Algorithms. $3 184661
700 1 $a Szpankowski, Wojciech, $d 1952- $e author. $3 968856