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Abstract: In certain classication problems there is a strong asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classication algorithms require some information about all classes involved, label estimation for the un-represented class is... (Update)
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BibTeX entry: (Update)
@article{ cz02asymmetric,
author = "Aleksander Ko{\l}cz and Joshua Alspector",
title = "Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking",
journal = "Information Retrieval",
volume = "5",
number = "1",
publisher = "Kluwer Academic Publishers",
pages = "5--40",
year = "2002",
url = "citeseer.ist.psu.edu/750477.html" }
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