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In PU learning, two sets of examples are assumed to be available for training: the positive set P and a mixed set U, which is assumed to contain both positive and negative samples, but without these being labeled as such.
This algorithm appears to fit precisely the issue of classifying cluster members based on field star regions acting as training sets.
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