A linear functional strategy for regularized ranking

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dc.contributor.authorKriukova, Galyna
dc.contributor.authorPanasiuk, Oleksandra
dc.contributor.authorPereverzyev, Sergei V.
dc.contributor.authorTkachenko, Pavlo
dc.date.accessioned2017-06-14T14:27:38Z
dc.date.available2017-06-14T14:27:38Z
dc.date.issued2016
dc.description.abstractRegularization schemes are frequently used for performing ranking tasks. This topic has been intensively studied in recent years. However, to be effective a regularization scheme should be equipped with a suitable strategy for choosing a regularization parameter. In the present study we discuss an approach, which is based on the idea of a linear combination of regularized rankers corresponding to different values of the regularization parameter. The coefficients of the linear combination are estimated by means of the so-called linear functional strategy. We provide a theoretical justification of the proposed approach and illustrate them by numerical experiments. Some of them are related with ranking the risk of nocturnal hypoglycemia of diabetes patients.en
dc.identifier.citationA linear functional strategy for regularized ranking / Galyna Kriukova, Oleksandra Panasiuk, Sergei V. Pereverzyev, Pavlo Tkachenko // Neural Networks. - 2016. - Vol. 73, January. - P. 26-35.uk
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/11577
dc.identifier.urihttp://dx.doi.org/10.1016/j.neunet.2015.08.012en
dc.language.isoenuk
dc.relation.sourceNeural Networksuk
dc.statuspublished earlieruk
dc.subjectregularization Ill-posed problemen
dc.subjectrankingen
dc.subjectlinear functional strategyen
dc.subjectdiabetes technologyen
dc.titleA linear functional strategy for regularized rankingen
dc.typeArticleuk
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