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This paper proposes a novel algorithm for signal classification problems. The authors consider a non-stationary random signal, where samples can be classified into several different classes, and samples in each class are identically independently distributed with an unknown probability distribution. The problem to be solved is to estimate the probability distributions of the classes and the correct membership of the samples to the classes. They propose a signal classification method based on the data compression principle that the accurate estimation in the classification problems induces the optimal signal models for data compression.
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