Multi-Kernel Gaussian Processes

@inproceedings{Melkumyan2011MultiKernelGP,
  title={Multi-Kernel Gaussian Processes},
  author={Arman Melkumyan and Fabio Tozeto Ramos},
  booktitle={IJCAI},
  year={2011}
}
Although Gaussian process inference is usually formulated for a single output, in many machine learning problems the objective is to infer multiple tasks jointly, possibly exploring the dependencies between them to improve results. Real world examples of this problem include ore mining where the objective is to infer the concentration of several chemical components to assess the ore quality. Similarly, in robotics and control problems there are more than one actuator and the understanding and… CONTINUE READING
Highly Cited
This paper has 77 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 32 extracted citations

Remarks on multi-output Gaussian process regression

Knowl.-Based Syst. • 2018
View 14 Excerpts
Highly Influenced

Multi-modal fetal ECG extraction using multi-kernel Gaussian processes

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) • 2017
View 3 Excerpts

77 Citations

0102030'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 77 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 11 references

Gaussian Processes for Machine Learning

Advanced Lectures on Machine Learning • 2009
View 2 Excerpts

Semiparametric latent factor models

M. Seeger Y. W. Teh
Advances in Neural Information Processing Systems • 2006

Fast sparse gaussian process methods : The information vector machine

N. Lawrence, M. Seeger
The Elements of Statistical Learning • 2001

Ghahra - mani . Sparse gaussian processes using pseudoinputs

E. Snelson, Z.
Advances in Neural Information Processing Systems