Matches in UGent Biblio for { <https://biblio.ugent.be/publication/3132065#aggregation> ?p ?o. }
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- aggregation classification "A1".
- aggregation creator B765557.
- aggregation creator B765558.
- aggregation creator B765559.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 3132065.bibtex.
- aggregation hasFormat 3132065.csv.
- aggregation hasFormat 3132065.dc.
- aggregation hasFormat 3132065.didl.
- aggregation hasFormat 3132065.doc.
- aggregation hasFormat 3132065.json.
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- aggregation isPartOf urn:issn:1063-6706.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Science General".
- aggregation title "A kernel-based framework for learning graded relations from data".
- aggregation abstract "Driven by a large number of potential applications in areas, such as bioinformatics, information retrieval, and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data. The results indicate that incorporating domain knowledge about relations improves the predictive performance.".
- aggregation authorList BK1135549.
- aggregation endPage "1101".
- aggregation issue "6".
- aggregation startPage "1090".
- aggregation volume "20".
- aggregation aggregates 3132076.
- aggregation isDescribedBy 3132065.
- aggregation similarTo TFUZZ.2012.2194151.
- aggregation similarTo LU-3132065.