title: A linear functional strategy for regularized ranking.
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.
authors.affiliation: Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Altenbergerstrasse 69, 4040 Linz, Austria.
authors.forename: Galyna
authors.initials: G
authors.lastname: Kriukova
authors.affiliation: Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Altenbergerstrasse 69, 4040 Linz, Austria.
authors.forename: Oleksandra
authors.initials: O
authors.lastname: Panasiuk
authors.affiliation: Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Altenbergerstrasse 69, 4040 Linz, Austria.
authors.forename: Sergei V
authors.initials: SV
authors.lastname: Pereverzyev
authors.affiliation: Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Altenbergerstrasse 69, 4040 Linz, Austria. Electronic address: pavlo.tkachenko@oeaw.ac.at.
authors.forename: Pavlo
authors.initials: P
authors.lastname: Tkachenko
mesh_terms: D000465:Algorithms; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis; D016018:Least-Squares Analysis; D016014:Linear Models; D000069550:Machine Learning; D016571:Neural Networks, Computer; D018570:Risk Assessment
keywords: Diabetes technology; Ill-posed problem; Linear functional strategy; Ranking; Regularization