A linear functional strategy for regularized ranking

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Kriukova, Galyna
Panasiuk, Oleksandra
Pereverzyev, Sergei V.
Tkachenko, Pavlo
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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.
regularization Ill-posed problem, ranking, linear functional strategy, diabetes technology
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.