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  Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

Tsuda, K., Uda, S., Kin, T., & Asai, K. (2004). Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data. Neural Processing Letters, 19, 63-72. doi:10.1023/B:NEPL.0000016845.36307.d7.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DA67-C Version Permalink: http://hdl.handle.net/21.11116/0000-0005-4F80-2
Genre: Journal Article

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 Creators:
Tsuda, K1, 2, Author              
Uda, S, Author
Kin, T, Author
Asai, K, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In biological data, it is often the case that objects are described in two or more representations. In order to perform classification based on such data, we have to combine them in a certain way. In the context of kernel machines, this task amounts to mix several kernel matrices into one. In this paper, we present two ways to mix kernel matrices, where the mixing weights are optimized to minimize the cross validation error. In bacteria classification and gene function prediction experiments, our methods significantly outperformed single kernel classifiers in most cases.

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 Dates: 2004-02
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2548
DOI: 10.1023/B:NEPL.0000016845.36307.d7
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Title: Neural Processing Letters
Source Genre: Journal
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Publ. Info: Brussels, Belgium : D facto
Pages: - Volume / Issue: 19 Sequence Number: - Start / End Page: 63 - 72 Identifier: ISSN: 1370-4621
CoNE: https://pure.mpg.de/cone/journals/resource/954927002246