On the convergence rate and some applications of regularized ranking algorithms
| Autor: | Pavlo Tkachenko, Sergei V. Pereverzyev, Galyna Kriukova |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Numerical Analysis Class (set theory) Mathematical optimization Control and Optimization Algebra and Number Theory Computer science Quantitative Biology::Tissues and Organs Applied Mathematics General Mathematics 010102 general mathematics Context (language use) 010103 numerical & computational mathematics 01 natural sciences Ranking (information retrieval) Regularization theory Rate of convergence Ranking SVM Learning to rank 0101 mathematics |
| Zdroj: | Journal of Complexity. 33:14-29 |
| ISSN: | 0885-064X |
| DOI: | 10.1016/j.jco.2015.09.004 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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