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Finding the utility function is a [[Regression analysis|regression]] learning problem which is well developed in machine learning.
===<span class="khlinks"><a lang="en" href="http://sherpa-plus.com/topic/en/Preference" style="color:rgb(0, 0, 0)!important;font-size:11px!important;font-weight:normal!important">Preference</a></span> relations=== The binary representation of preference information is called preference relation. For each pair of alternatives (instances or labels), a binary predicate can be learned by conventional supervising learning approach. Fünkranz, Johannes and Hülermeier proposed this approach in label ranking problem.<ref name="FURN03" /> For object ranking, there is an early approach by Cohen et al.<ref name="COHE98" /> Using preference relations to predict the ranking will not be so intuitive. Since preference relation is not transitive, it implies that the solution of ranking satisfying those relations would sometimes be unreachable or more than one solution. A more common approach is to find a ranking solution which is maximally consistent with the preference relations. This approach is a natural extension of pairwise classification.<ref name="FURN03" />▼
▲Using preference relations to predict the ranking will not be so intuitive. Since preference relation is not transitive, it implies that the solution of ranking satisfying those relations would sometimes be unreachable or more than one solution. A more common approach is to find a ranking solution which is maximally consistent with the preference relations. This approach is a natural extension of pairwise classification.<ref name="FURN03" />
==Uses==
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