A Dynamic Data Structure for Reverse Lexicographically Sorted Prefixes
CPM '99 Proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching
Estimating Entropy Rates with Bayesian Confidence Intervals
Neural Computation
Fast gapped variants for Lempel--Ziv--Welch compression
Information and Computation
Achievable complexity-performance tradeoffs in lossy compression
Problems of Information Transmission
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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 compression: the Shannon entropy. We then discuss applications of pattern matching and universal compression to universal prediction, classification, and entropy estimation