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2004
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Dealing with different distributions in learning from

14 years 11 months ago
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. General Terms Algorithms, Experimentation Keywords Classification, positive and unlabeled learning, EM
Xiaoli Li, Bing Liu
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2004
Where WWW
Authors Xiaoli Li, Bing Liu
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