On the optimality of universal classifiers for finite-length individual test sequences
J Ziv - arXiv preprint arXiv:0909.4233, 2009 - arxiv.org
We consider pairs of finite-length individual sequences that are realizations of unknown,
finite alphabet, stationary sources in a clas M of sources with vanishing memory (eg
stationary Markov sources). The task of a universal classifier is to decide whether the two
sequences are emerging from the same source or are emerging from two distinct sources in
M, and it has to carry this task without any prior knowledge of the two underlying probability
measures. Given a fidelity function and a fidelity criterion, the probability of classification …
finite alphabet, stationary sources in a clas M of sources with vanishing memory (eg
stationary Markov sources). The task of a universal classifier is to decide whether the two
sequences are emerging from the same source or are emerging from two distinct sources in
M, and it has to carry this task without any prior knowledge of the two underlying probability
measures. Given a fidelity function and a fidelity criterion, the probability of classification …
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