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| Authors: | Tsuda Koji Uda Shinsuke Kin Taishin Asai Kiyoshi |
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| Affiliation: | (1) AIST, Computational Biology Research Center, 2–41–6 Aomi Koto-ku, Tokyo, 135–0064, Japan. e-mail;(2) Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tubingen, Germany;(3) Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 2268502, Japan;(4) Department of Computational Biology, Graduate School of Frontier Science, University of Tokyo, 5–1–5, Kashiwanoha, Kashiwa, 2778562, Japan |
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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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| Keywords: | bacteria classification bioinformatics kernel machines mixing kernel matrices |
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