On the role of pattern matching in information theory
AD Wyner, J Ziv, AJ Wyner - IEEE Transactions on information …, 1998 - ieeexplore.ieee.org
In this paper, the role of pattern matching in information theory is motivated and discussed.
We describe the relationship between a pattern's recurrence time and its probability under
the data-generating stochastic source. We show how this relationship has led to great
advances in universal data compression. We then describe nonasymptotic uniform bounds
on the performance of data-compression algorithms in cases where the size of the training
data that is available to the encoder is not large enough so as to yield the asymptotic …
We describe the relationship between a pattern's recurrence time and its probability under
the data-generating stochastic source. We show how this relationship has led to great
advances in universal data compression. We then describe nonasymptotic uniform bounds
on the performance of data-compression algorithms in cases where the size of the training
data that is available to the encoder is not large enough so as to yield the asymptotic …
On the Role of Pattern Matching in Information Theory
S Verd, SW McLaughlin - 2000 - ieeexplore.ieee.org
In this paper, the role of pattern matching information theory is motivated and discussed. We
describe the relationship between a pattern's recurrence time and its probability under the
data-generating stochastic source. We show how this relationship has led to great advances
in universal data compression. We then describe nonasymptotic uniform bounds on the
performance of data-compression algorithms in cases where the size of the training data that
is available to the encoder is not large enough so as to yield the asymptotic compression …
describe the relationship between a pattern's recurrence time and its probability under the
data-generating stochastic source. We show how this relationship has led to great advances
in universal data compression. We then describe nonasymptotic uniform bounds on the
performance of data-compression algorithms in cases where the size of the training data that
is available to the encoder is not large enough so as to yield the asymptotic compression …
Showing the best results for this search. See all results
