Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Nickisch, H; Seeger, M

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_1788304_4

DetailsSummary

Released

Report

Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

MPS-Authors

/persons/resource/persons84109

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

External Resource

https://arxiv.org/abs/1103.0897
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

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.