Please use this identifier to cite or link to this item:
|Title:||Learning classifiers without negative examples: A reduction approach|
|Source:||Zhang, D.,Lee, W.S. (2008). Learning classifiers without negative examples: A reduction approach. 3rd International Conference on Digital Information Management, ICDIM 2008 : 638-643. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDIM.2008.4746761|
|Abstract:||The problem of PU Learning, i.e., learning classifiers with positive and unlabelled examples (but not negative examples), is very important in information retrieval and data mining. We address this problem through a novel approach: reducing it to the problem of learning classifiers for some meaningful multivariate performance measures. In particular, we show how a powerful machine learning algorithm, Support Vector Machine, can be adapted to solve this problem. The effectiveness and efficiency of the proposed approach have been confirmed by our experiments on three real-world datasets. ©2008 IEEE.|
|Source Title:||3rd International Conference on Digital Information Management, ICDIM 2008|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 23, 2018
checked on Jan 20, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.