This paper is a collection of numerous methods and results concerning a design of kernel functions. It gives a short overview of methods of building kernels in metric spaces, especially and . However we also present a new theory. Introducing kernels was motivated by searching for non-linear patterns by using linear functions in a feature space created using a non-linear feature map.
@article{bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1127,
author = {Tomasz G\'orecki and Maciej \L uczak},
title = {Some methods of constructing kernels in statistical learning},
journal = {Discussiones Mathematicae Probability and Statistics},
volume = {30},
year = {2010},
pages = {179-201},
zbl = {1272.62049},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1127}
}
Tomasz Górecki; Maciej Łuczak. Some methods of constructing kernels in statistical learning. Discussiones Mathematicae Probability and Statistics, Tome 30 (2010) pp. 179-201. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1127/
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