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- W289679716 abstract "This work focuses on label ranking, a particular task of preference learning, wherein the problem is to learn a mapping from instances to rankings over a finite set of labels. This paper discusses and proposes alternative reduction techniques that decompose the original problem into binary classification related to pairs of labels and that can take into account label correlation during the learning process." @default.
- W289679716 created "2016-06-24" @default.
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- W289679716 date "2014-01-01" @default.
- W289679716 modified "2023-09-27" @default.
- W289679716 title "Alternative Decomposition Techniques for Label Ranking" @default.
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- W289679716 doi "https://doi.org/10.1007/978-3-319-08855-6_47" @default.
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