On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods
Machine Learning
2013-01-30 v1 Machine Learning
Abstract
It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.
Cite
@article{arxiv.1301.6944,
title = {On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods},
author = {Andreas Christmann and Robert Hable},
journal= {arXiv preprint arXiv:1301.6944},
year = {2013}
}
Comments
13 pages