Dealing with different distributions in learning from

@inproceedings{Li2004DealingWD,
  title={Dealing with different distributions in learning from},
  author={Xiaoli Li and Bing Liu},
  booktitle={WWW},
  year={2004}
}
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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