Label ranking by learning pairwise preferences

@article{Hllermeier2008LabelRB,
  title={Label ranking by learning pairwise preferences},
  author={Eyke H{\"u}llermeier and Johannes F{\"u}rnkranz and Weiwei Cheng and Klaus Brinker},
  journal={Artif. Intell.},
  year={2008},
  volume={172},
  pages={1897-1916}
}
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TLDR
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TLDR
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References

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A key advantage of such a decomposition, namely the fact that the learner can be adapted to different loss functions by using different ranking procedures on the same underlying order relations, is elaborated on.
Ranking by pairwise comparison a note on risk minimization
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A potential application of the ranking by pairwise comparison method in (qualitative) fuzzy classification is outlined by outlining a potential application and identifying some extensions necessary in this context.
Pairwise Preference Learning and Ranking
TLDR
The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example.
A Unified Model for Multilabel Classification and Ranking
TLDR
This work proposes a suitable extension of label ranking that incorporates the calibrated scenario, and suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique.
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TLDR
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TLDR
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