A linear functional strategy for regularized ranking

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dc.contributor.author Kriukova, Galyna
dc.contributor.author Panasiuk, Oleksandra
dc.contributor.author Pereverzyev, Sergei V.
dc.contributor.author Tkachenko, Pavlo
dc.date.accessioned 2017-06-14T14:27:38Z
dc.date.available 2017-06-14T14:27:38Z
dc.date.issued 2016
dc.identifier.citation A 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.uri http://ekmair.ukma.edu.ua/handle/123456789/11577
dc.identifier.uri http://dx.doi.org/10.1016/j.neunet.2015.08.012 en
dc.description.abstract Regularization 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.language.iso en uk
dc.subject regularization Ill-posed problem en
dc.subject ranking en
dc.subject linear functional strategy en
dc.subject diabetes technology en
dc.title A linear functional strategy for regularized ranking en
dc.type Article uk
dc.status published earlier uk
dc.relation.source Neural Networks uk

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