MiS Preprint Repository
We have decided to discontinue the publication of preprints on our preprint server end of 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.
Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods
Christian Rieger and Barbara ZwicknaglAbstract
This paper introduces a new technique for the analysis of kernel-based regression problems. The basic tools are sampling inequalities which apply to all machine learning problems involving penalty terms induced by kernels related to Sobolev spaces. They lead to explicit deterministic results concerning the worst case behaviour of
- Received:
- 15.12.06
- Published:
- 15.12.06
- Keywords:
- sampling inequality, radial basis functions, approximation theory, reproducing kernel Hilbert space, Sobolev space