DocumentCode
2974687
Title
A universal prediction lemma and applications to universal data compression
Author
Ziv, Jacob
Author_Institution
Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel
fYear
1999
fDate
1999
Firstpage
2
Lastpage
5
Abstract
A universal prediction lemma is derived for the class of conditional probability measures that are limited to conditioning events that occur in the training data. The lemma is then used to derive lower bounds on the efficiency of a number of universal data compression algorithms. These bounds are non-asymptotic in the sense that they express the effect of limited training data on the compression efficiency
Keywords
data compression; prediction theory; probability; conditional probability measures; conditioning events; non-asymptotic bounds; training data; universal data compression; universal prediction lemma; Character generation; Data compression; Electric variables measurement; Entropy; Frequency measurement; Jacobian matrices; Minimax techniques; Statistics; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Conference_Location
Caesarea
Print_ISBN
0-7695-0140-0
Type
conf
DOI
10.1109/HOST.1999.778680
Filename
778680
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