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

• Type: Conference
• Year: 2004
• Where: WWW
• Authors: Xiaoli Li, Bing Liu
• Keywords: Hidden Positive Examples, Internet Technology, Unlabeled Examples, Unlabeled Learning, WWW 2004