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|Title:||Dealing with different distributions in learning from positive and unlabeled web data||Authors:||Li, X.
Positive and unlabeled learning
|Issue Date:||2004||Citation:||Li, X.,Liu, B. (2004). Dealing with different distributions in learning from positive and unlabeled web data. Thirteenth International World Wide Web Conference Proceedings, WWW2004 : 1172-1173. ScholarBank@NUS Repository.||Abstract:||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.||Source Title:||Thirteenth International World Wide Web Conference Proceedings, WWW2004||URI:||http://scholarbank.nus.edu.sg/handle/10635/114656||ISBN:||158113844X|
|Appears in Collections:||Staff Publications|
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