Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/77983
Title: A simple probabilistic approach to learning from positive and unlabeled examples
Authors: Zhang, D.
Lee, W.S. 
Issue Date: 2005
Citation: Zhang, D.,Lee, W.S. (2005). A simple probabilistic approach to learning from positive and unlabeled examples. Proceedings of the 2005 UK Workshop on Computational Intelligence, UKCI 2005 : 83-87. ScholarBank@NUS Repository.
Abstract: We propose a simple probabilistic approach to learning from positive and unlabeled examples, and show experimentally that it can approximate or outperform other state-ofthe- Art approaches to this problem in spite of its simplicity. By employing a linear-time learning algorithm such as PrTFIDF, our approach can be highly efficient and scalable.
Source Title: Proceedings of the 2005 UK Workshop on Computational Intelligence, UKCI 2005
URI: http://scholarbank.nus.edu.sg/handle/10635/77983
Appears in Collections:Staff Publications

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