Mercer's Theorem, Feature Maps, and Smoothing
Abstract
We study Mercer’s theorem and feature maps for several positive definite kernels that are widely used in practice. The smoothing properties of these kernels will also be explored.
Cite
Text
Minh et al. "Mercer's Theorem, Feature Maps, and Smoothing." Annual Conference on Computational Learning Theory, 2006. doi:10.1007/11776420_14Markdown
[Minh et al. "Mercer's Theorem, Feature Maps, and Smoothing." Annual Conference on Computational Learning Theory, 2006.](https://mlanthology.org/colt/2006/minh2006colt-mercer/) doi:10.1007/11776420_14BibTeX
@inproceedings{minh2006colt-mercer,
title = {{Mercer's Theorem, Feature Maps, and Smoothing}},
author = {Minh, Ha Quang and Niyogi, Partha and Yao, Yuan},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2006},
pages = {154-168},
doi = {10.1007/11776420_14},
url = {https://mlanthology.org/colt/2006/minh2006colt-mercer/}
}