Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/114656
Title: Dealing with different distributions in learning from positive and unlabeled web data
Authors: Li, X. 
Liu, B.
Keywords: Classification
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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