Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neunet.2015.08.012 | Neural Networks |
Keywords | Field | DocType |
Regularization,Ill-posed problem,Ranking,Linear functional strategy,Diabetes technology | Least squares,Linear combination,Mathematical optimization,Linear form,Ranking,Linear model,Backus–Gilbert method,Regularization (mathematics),Artificial intelligence,Machine learning,Mathematics,Regularization perspectives on support vector machines | Journal |
Volume | Issue | ISSN |
73 | C | 0893-6080 |
Citations | PageRank | References |
3 | 0.39 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Galyna Kriukova | 1 | 3 | 0.73 |
| Oleksandra Panasiuk | 2 | 3 | 0.39 |
| Sergei V. Pereverzyev | 3 | 3 | 0.39 |
| Pavlo Tkachenko | 4 | 8 | 4.26 |