Optimal Learning Rates for Least Squares SVMs Using Gaussian Kernels
Mona Eberts, Ingo Steinwart
Advances in Neural Information Processing Systems (NeurIPS), pp. 1539–1547, 2011.
Abstract
We prove a new oracle inequality for support vector machines with Gaussian RBF kernels solving the regularized least squares regression problem. To this end, we apply the modulus of smoothness. With the help of the new oracle inequality we then derive learning rates that can also be achieved by a simple data-dependent parameter selection method. Finally, it turns out that our learning rates are asymptotically optimal for regression functions satisfying certain standard smoothness conditions.Links
BibTeX
@inproceedings{eberts11_neurips,
title = {Optimal Learning Rates for Least Squares {SVM}s Using {G}aussian Kernels},
author = {Eberts, Mona and Steinwart, Ingo},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
volume = {24},
pages = {1539--1547},
url = {https://proceedings.neurips.cc/paper/2011/file/51ef186e18dc00c2d31982567235c559-Paper.pdf}
}