A Kernel Approach for Learning from Almost Orthogonal Patterns
Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well. We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine.
Cite
Text
Schölkopf et al. "A Kernel Approach for Learning from Almost Orthogonal Patterns." European Conference on Machine Learning, 2002. doi:10.1007/3-540-36755-1_44Markdown
[Schölkopf et al. "A Kernel Approach for Learning from Almost Orthogonal Patterns." European Conference on Machine Learning, 2002.](https://mlanthology.org/ecmlpkdd/2002/scholkopf2002ecml-kernel/) doi:10.1007/3-540-36755-1_44BibTeX
@inproceedings{scholkopf2002ecml-kernel,
title = {{A Kernel Approach for Learning from Almost Orthogonal Patterns}},
author = {Schölkopf, Bernhard and Weston, Jason and Eskin, Eleazar and Leslie, Christina S. and Noble, William Stafford},
booktitle = {European Conference on Machine Learning},
year = {2002},
pages = {511-528},
doi = {10.1007/3-540-36755-1_44},
url = {https://mlanthology.org/ecmlpkdd/2002/scholkopf2002ecml-kernel/}
}