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It is shown that encoding the query can change the circuit's hypothesis class over the signal by modulating how its existing signal frequencies are combined, without introducing new sample-dependent information.
A metric for binary classification tasks, the class margin, is introduced, which analytically connects data-induced randomness with classification accuracy for a given data-embedding map, demonstrating its ability to identify data-induced randomness that hinders classification performance.
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