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A key assumption underlying traditional supervised learning algorithms is that labeled examples used to train a classifier are drawn i.i.d. from the same distribution as test samples. This assumption is violated when classifying a test sample whose statistics differ from the training samples because the test signal is the output of a noisy linear time-invariant system, e.g., from channel propagation...
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