Kernel Methods in Bioinformatics

Borgwardt, Karsten M

doi:10.1007/978-3-642-16345-6_15

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Kernel Methods in Bioinformatics

Borgwardt, K. M. (2011). Kernel Methods in Bioinformatics. In H.-H.-S. Lu, B. Schölkopf, & H. Zhao (Eds.), Handbook of Statistical Bioinformatics (pp. 317-334). Berlin, Heidelberg: Springer.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-F386-A Version Permalink: https://hdl.handle.net/21.11116/0000-000C-F387-9

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Creators:
Borgwardt, Karsten M¹, Author

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1Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647

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Free keywords: Kernel Method Multiple Kernel Learning Prediction Task Protein Function Prediction Support Vector Machine

Abstract: Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.

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Dates: Date issued: 2011

Publication Status: Issued

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Identifiers: DOI: 10.1007/978-3-642-16345-6_15

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Title: Handbook of Statistical Bioinformatics

Source Genre: Book

Creator(s):
Lu, Henry Horng-Shing, Editor
Schölkopf, Bernhard, Editor
Zhao, Hongyu, Editor

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Publ. Info: Berlin, Heidelberg : Springer

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 317 - 334 Identifier: ISBN: 978-3-642-16345-6

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Title: Springer Handbooks of Computational Statistics

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