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portada Analytic Information Theory: From Compression to Learning
Type
Physical Book
Publisher
Author
Language
English
Pages
400
Format
Hardcover
ISBN13
9781108474443
Edition No.
1

Analytic Information Theory: From Compression to Learning

Michael Drmota; Wojciech Szpankowski (Author) · Cambridge University Press · Hardcover

Analytic Information Theory: From Compression to Learning - Michael Drmota; Wojciech Szpankowski

New Book Imported to Taiwan
Delivery: 11 Sep - 21 Sep Shipping: 5 to 6 business days.
NT$ 6,422
NT$ 6,422

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Synopsis "Analytic Information Theory: From Compression to Learning "

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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