Refining Kernels for Regression and Uneven Classification Problems
Abstract
Kernel alignment has recently been proposed as a method for measuring the degree of agreement between a kernel and a classification learning task. In this paper we extend the notion of kernel alignment to two other common learning problems: regression and classification with uneven data. We present a modified definition of alignment together with a novel theoretical justification for why improving alignment will lead to better performance in the regression case. Experimental evidence is provided to show that improving the alignment leads to a reduction in generalization error of standard regressors and classifiers.
Cite
Text
Kandola and Shawe-Taylor. "Refining Kernels for Regression and Uneven Classification Problems." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.Markdown
[Kandola and Shawe-Taylor. "Refining Kernels for Regression and Uneven Classification Problems." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.](https://mlanthology.org/aistats/2003/kandola2003aistats-refining/)BibTeX
@inproceedings{kandola2003aistats-refining,
title = {{Refining Kernels for Regression and Uneven Classification Problems}},
author = {Kandola, Jaz S. and Shawe-Taylor, John},
booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics},
year = {2003},
pages = {157-162},
volume = {R4},
url = {https://mlanthology.org/aistats/2003/kandola2003aistats-refining/}
}