Name
Abstract | ||
|---|---|---|
This paper studies the ranking problem in the context of the regularization theory that allows a simultaneous analysis of a wide class of ranking algorithms. Some of them were previously studied separately. For such ones, our analysis gives a better convergence rate compared to the reported in the literature. We also supplement our theoretical results with numerical illustrations and discuss the application of ranking to the problem of estimating the risk from errors in blood glucose measurements of diabetic patients. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.jco.2015.09.004 | Journal of Complexity |
Keywords | Field | DocType |
Ranking,Convergence rate,Source condition,Blood glucose error grid | Learning to rank,Mathematical optimization,Ranking SVM,Ranking,Computer science,Rate of convergence,Regularization theory | Journal |
Volume | Issue | ISSN |
33 | C | 0885-064X |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Galyna Kriukova | 1 | 1 | 0.36 |
| Sergei V. Pereverzyev | 2 | 20 | 4.29 |
| Pavlo Tkachenko | 3 | 8 | 4.26 |