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  Cross-Validation Optimization for Structured Hessian Kernel Methods

Seeger, M.(2006). Cross-Validation Optimization for Structured Hessian Kernel Methods. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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kerlogregr_3863[0].pdf (Publisher version), 367KB
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Seeger, M1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We address the problem of learning hyperparameters in kernel methods for which the Hessian of the objective is structured. We propose an approximation to the cross-validation log likelihood whose gradient can be computed analytically, solving the hyperparameter learning problem efficiently through nonlinear optimization. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels or to large datasets. When applied to the problem of multi-way classification, our method scales linearly in the number of classes and gives rise to state-of-the-art results on a remote imaging task.

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 Dates: 2006-02
 Publication Status: Published in print
 Pages: 36
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: BibTex Citekey: 3863
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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