Alignment based kernel learning with a continuous set of base kernels
- Arash Afkanpour
- Csaba Szepesvari, Department of Computing Science; PI of AICML
- Michael Bowling, University of Alberta
The success of kernel-based learning methods depends on the choice of kernel. Recently, kernel learning methods have been proposed that use data to select the most appropriate kernel, usually by combining a set of base kernels. We introduce a new algorithm for kernel learning that combines a continuous set of base kernels, without the common step of discretizing the space of base kernels. We demonstrate that our new method achieves stateof-the-art performance across a variety of real-world datasets. Furthermore, we explicitly demonstrate the importance of combining the right dictionary of kernels, which is problematic for methods that combine a finite set of base kernels chosen a priori. Our method is not the first approach to work with continuously parameterized kernels. We adopt a twostage kernel learning approach. We also show that our method requires substantially less computation than previous such approaches, and so is more amenable to multi-dimensional parameterizations of base kernels, which we demonstrate.
Citation
A. Afkanpour, C. Szepesvari, M. Bowling. "Alignment based kernel learning with a continuous set of base kernels". Machine Learning, 91, pp 305–324, May 2013.| Keywords: | Two-stage kernel learning, Continuous kernel sets |
| Category: | In Journal |
| Web Links: | doi |
BibTeX
@article{Afkanpour+al:13,
author = {Arash Afkanpour and Csaba Szepesvari and Michael Bowling},
title = {Alignment based kernel learning with a continuous set of base
kernels},
Volume = "91",
Pages = {305–324},
journal = {Machine Learning},
year = 2013,
}Last Updated: July 13, 2020Submitted by Sabina P
