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

Tsuda, K; Uda, S; Kin, T; Asai, K

doi:10.1023/B:NEPL.0000016845.36307.d7

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

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

MPS-Authors

/persons/resource/persons84265

Tsuda, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://link.springer.com/content/pdf/10.1023%2FB%3ANEPL.0000016845.36307.d7.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DA67-C

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.