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  Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Nickisch, H., & Seeger, M.(2011). Multiple Kernel Learning: A Unifying Probabilistic Viewpoint. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BC70-8 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-93A7-B
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 Creators:
Nickisch, H1, Author           
Seeger, M, Author           
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1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the marginal likelihood and contains many regularised risk approaches as special cases. Furthermore, we derive an efficient and provably convergent optimisation algorithm.

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 Dates: 2011-03
 Publication Status: Issued
 Pages: 12
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: BibTex Citekey: NickischS2011
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Title: Technical Reports of the Max Planck Institute for Biological Cybernetics
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