On asymptotically optimal methods of prediction and adaptive coding for Markov sources
Journal of Complexity
How to Achieve Minimax Expected Kullback-Leibler Distance from an Unknown Finite Distribution
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Improved Behaviour of Tries by the "Symmetrization" of the Source
DCC '02 Proceedings of the Data Compression Conference
Data Coding by Linear Forms of Numerical Sequences
Cybernetics and Systems Analysis
Redundancy estimates for the Lempel-Ziv algorithm of data compression
Discrete Applied Mathematics
Merged Dictionary Code Compression for FPGA Implementation of Custom Microcoded PEs
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
On perfect codes for an additive channel
Problems of Information Transmission
IEEE Transactions on Information Theory
On Finding Predictors for Arbitrary Families of Processes
The Journal of Machine Learning Research
Tunstall code, Khodak variations, and random walks
IEEE Transactions on Information Theory
On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem
The Journal of Machine Learning Research
An information-theoretic approach to estimate the capacity of processing units
Performance Evaluation
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From the Publisher:This volume constitutes a comprehensive self-contained course on source encoding. This is a rapidly developing field and the purpose of this book is to present the theory from its beginnings to the latest developments, some of which appear in book form for the first time. The major differences between this volume and previously-published works is that, here, information retrieval is incorporated into source coding instead of discussing this separately. Secondly, this volume places an emphasis on the trade-off between complexity and the quality of coding, i.e. what is the price of achieving a maximum degree of data compression? Thirdly, special attention is paid to universal families which contain a good compressing map for every source in a set. The volume presents a new algorithm for retrieval, which is optimal with respect to both program length and running time, and algorithms for hashing and adaptive on-line compressing. All the main tools of source coding and data compression such as Shannon, Ziv-Lempel, Gilbert-Moore codes, Kolmogorov complexity and entropy, lexicographic and digital search, are discussed. Moreover, data compression methods are described for developing short programs for partially specified Boolean functions, short formulas for threshold functions, identification keys, stochastic algorithms for finding the occurrence of a word in a text, and T-independent sets. Researchers and graduate students of information theory and theoretical computer science. It will also serve as a useful reference for communication engineers and data base designers.