Text compression
The data compression book (2nd ed.)
The data compression book (2nd ed.)
Neural Computation
Text compression via alphabet re-representation
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Fast Text Compression with Neural Networks
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
A universal algorithm for sequential data compression
IEEE Transactions on Information Theory
Sequential neural text compression
IEEE Transactions on Neural Networks
Online Symbolic-Sequence Prediction with Discrete-Time Recurrent Neural Networks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
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Arithmetic coding is one of the most outstanding techniques for lossless data compression. It attains its good performance with the help of a probability model which indicates at each step the probability of occurrence of each possible input symbol given the current context. The better this model, the greater the compression ratio achieved. This work analyses the use of discrete-time recurrent neural networks and their capability for predicting the next symbol in a sequence in order to implement that model. The focus of this study is on online prediction, a task much harder than the classical offline grammatical inference with neural networks. The results obtained show that recurrent neural networks have no problem when the sequences come from the output of a finite-state machine, easily giving high compression ratios. When compressing real texts, however, the dynamics of the sequences seem to be too complex to be learned online correctly by the net.