at Google ScholarTitle data
Christmann, Andreas ; Zhou, Ding-Xuan:
Learning rates for the risk of kernel-based quantile regression estimators in additive models.
In: Analysis and Applications.
Vol. 14
(2016)
Issue 3
.
- pp. 449-477.
ISSN 0219-5305
DOI: https://doi.org/10.1142/S0219530515500050
Official URL: 
Related URLs
Project information
| Project title: |
Project's official title Project's id Support Vector Machines bei stochastischer Abhaengigkeit (German)
Support Vector Machines under stochastic dependency CH/291/2-1 |
|---|---|
| Project financing: |
Deutsche Forschungsgemeinschaft Deutsche Forschungsgesellschaft |
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | Additive model; quantile regression; rate of convergence; support vector machine |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics > Chair Mathematics VII - Stochastics - Univ.-Prof. Dr. Andreas Christmann Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 310 Statistics 500 Science > 510 Mathematics |
| Date Deposited: | 28 Oct 2015 07:22 |
| Last Modified: | 13 Jun 2016 08:50 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/20514 |