A linear functional strategy for regularized ranking. (January 2016)

• Record Type: Journal Article
• Authors: Kriukova, Galyna; Panasiuk, Oleksandra; Pereverzyev, Sergei V.; Tkachenko, Pavlo
• Is Part Of: Neural networks. Volume 73(2016)
• Page Start: 26
• Page End: 35
• Publication Date: 2016-01
• Subjects: Regularization -- Ill-posed problem -- Ranking -- Linear functional strategy -- Diabetes technology; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.08.012
• Languages: English
• ISSNs: 0893-6080

Abstract

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.