Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Nickisch, H; Seeger, M

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Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

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Nickisch, H
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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https://arxiv.org/abs/1103.0897
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MPIK-TR-2011-Nickisch.pdf
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Citation

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

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BC70-8

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.