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Ranking by Pairwise Comparison: A Note on Risk Minimization  (Make Corrections)  
Eyke Hüllermeier, Johannes Fürnkranz



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Abstract: In this paper we consider the problem of learning ranking functions in a supervised manner. A ranking function is a mapping from instances to rankings over a finite number of labels and can thus be seen as an extension of a classification function. Our learning method, referred to as ranking by pairwise comparison (RPC), is a two-step procedure. First, a valued preference structure is induced from given preference data, using a natural extension of so-called pairwise classification. A ranking... (Update)

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BibTeX entry:   (Update)

@misc{ llermeier-ranking,
  author = "Eyke Hüllermeier and Johannes Fürnkranz",
  title = "Ranking by Pairwise Comparison: A Note on Risk Minimization",
  url = "citeseer.ist.psu.edu/683141.html" }
Citations (may not include all citations):
64   Fuzzy Preference Modelling and Multicriteria Decision Suppor.. (context) - Fodor, Roubens - 1994
43   Classification by pairwise coupling - Hastie, Tibshirani - 1998
11   Voting schemes for which it can be difficult to tell who won.. (context) - Bartholdi, Tovey et al. - 1989
11   Decision-making with a fuzzy preference relation (context) - Orlovski - 1978
9   Constraint classification: a new approach to multiclass clas.. (context) - Har-Peled, Roth et al. - 2002
6   Round robin classification (context) - Furnkranz - 2002
6   Handling possibilistic labels in pattern classification usin.. - Denoeux, Zouhal - 2001
4   Pairwise preference learning and ranking (context) - Furnkranz, Hullermeier - 2003
1   A fuzzy relation for group decision theory (context) - Bezdek, Spillman et al. - 1978
1   Twenty years of fuzzy preference structures (context) - De Baets, Fodor - 1997
1   Using a notion of acceptable in uncertain ordinal decision m.. (context) - Yager - 2002

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