document: Dealing with different distributions in learning from In the problem of learning with positive and unlabeled examples , existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are generated from the same distribution . This assumption may be violated in practice . In such cases , existing methods perform poorly . This paper proposes a novel technique A-EM to deal with the problem . Experimental results with product page classification demonstrate the effectiveness of the proposed technique .
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