Alignment based kernel learning with a continuous set of base kernels View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-06

AUTHORS

Arash Afkanpour, Csaba Szepesvári, Michael Bowling

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. More... »

PAGES

305-324

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10994-013-5361-8

DOI

http://dx.doi.org/10.1007/s10994-013-5361-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006366133


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