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  Large Scale Multiple Kernel Learning

Sonnenburg, S., Rätsch, G., Schäfer, C., & Schölkopf, B. (2006). Large Scale Multiple Kernel Learning. The Journal of Machine Learning Research, 7, 1531-1565.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D0DD-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-839D-7
Genre: Journal Article

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Sonnenburg, S, Author              
Rätsch, G1, Author              
Schäfer, C, Author
Schölkopf, B2, 3, Author              
Affiliations:
1Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million real-world splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox SHOGUN for which the source code is publicly available at http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun.

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 Dates: 2006-07
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: BibTex Citekey: 3994
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: 1531 - 1565 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1