Learning the Kernel with Hyperkernels
Abstract
This paper addresses the problem of choosing a kernel suitable for estimation with a support vector machine, hence further automating machine learning. This goal is achieved by defining a reproducing kernel Hilbert space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional.
Cite
Text
Ong et al. "Learning the Kernel with Hyperkernels." Journal of Machine Learning Research, 2005.Markdown
[Ong et al. "Learning the Kernel with Hyperkernels." Journal of Machine Learning Research, 2005.](https://mlanthology.org/jmlr/2005/ong2005jmlr-learning/)BibTeX
@article{ong2005jmlr-learning,
title = {{Learning the Kernel with Hyperkernels}},
author = {Ong, Cheng Soon and Smola, Alexander J. and Williamson, Robert C.},
journal = {Journal of Machine Learning Research},
year = {2005},
pages = {1043-1071},
volume = {6},
url = {https://mlanthology.org/jmlr/2005/ong2005jmlr-learning/}
}