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  Learning the Kernel with Hyperkernels

Ong, C., Smola, A., & Williamson, R. (2005). Learning the Kernel with Hyperkernels. The Journal of Machine Learning Research, 6, 1043-1071.

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 Creators:
Ong, CS1, 2, Author           
Smola, A, Author           
Williamson, R, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.
We state the equivalent
representer theorem for the choice of kernels and present a
semidefinite programming formulation of the resulting optimization
problem. Several recipes for constructing hyperkernels are provided, as
well as the details of common machine learning problems. Experimental
results for classification, regression and novelty
detection on UCI data show the feasibility of our approach.

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 Dates: 2005-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3512
 Degree: -

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Title: The Journal of Machine Learning Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 6 Sequence Number: - Start / End Page: 1043 - 1071 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1