Alignment based kernel learning with a continuous set of base kernels. Afkanpour, A., Szepesvári, C., & Bowling, M. H. Machine Learning, 91(3):305–324, Springer, 2013. Paper doi abstract bibtex 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 state-of-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 two-stage 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.
@article{AfSzeBo13,
abstract = {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 state-of-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 two-stage 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.},
author = {Afkanpour, A. and Szepesv{\'a}ri, Cs. and Bowling, M. H.},
date = {2013-06},
date-added = {2013-06-30 22:38:38 -0600},
date-modified = {2013-06-30 22:51:39 -0600},
doi = {10.1007/s10994-013-5361-8},
journal = {Machine Learning},
keywords = {multikernel learning; supervised learning},
number = {3},
pages = {305--324},
publisher = {Springer},
title = {Alignment based kernel learning with a continuous set of base kernels},
url_paper = {alignment_based_kernel_learning.pdf},
volume = {91},
year = {2013},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/s10994-013-5361-8}}
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