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- aggregation classification "C1".
- aggregation creator B126582.
- aggregation creator B126583.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 2914138.bibtex.
- aggregation hasFormat 2914138.csv.
- aggregation hasFormat 2914138.dc.
- aggregation hasFormat 2914138.didl.
- aggregation hasFormat 2914138.doc.
- aggregation hasFormat 2914138.json.
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- aggregation isPartOf urn:isbn:9783642158797.
- aggregation language "eng".
- aggregation publisher "Springer".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Mathematics and Statistics".
- aggregation title "Predicting partial orders: ranking with abstention".
- aggregation abstract "The prediction of structured outputs in general and rankings in particular has attracted considerable attention in machine learning in recent years, and different types of ranking problems have already been studied. In this paper, we propose a generalization or, say, relaxation of the standard setting, allowing a model to make predictions in the form of partial instead of total orders. We interpret such kind of prediction as a ranking with partial abstention: If the model is not sufficiently certain regarding the relative order of two alternatives and, therefore, cannot reliably decide whether the former should precede the latter or the other way around, it may abstain from this decision and instead declare these alternatives as being incomparable. We propose a general approach to ranking with partial abstention as well as evaluation metrics for measuring the correctness and completeness of predictions. For two types of ranking problems, we show experimentally that this approach is able to achieve a reasonable trade-off between these two criteria.".
- aggregation authorList BK325218.
- aggregation endPage "230".
- aggregation startPage "215".
- aggregation volume "6321".
- aggregation aggregates 2914209.
- aggregation isDescribedBy 2914138.
- aggregation similarTo 978-3-642-15880-3_20.
- aggregation similarTo LU-2914138.