Multi-Kernel Gaussian Processes

@inproceedings{Melkumyan2011MultiKernelGP,
  title={Multi-Kernel Gaussian Processes},
  author={A. Melkumyan and F. Ramos},
  booktitle={IJCAI},
  year={2011}
}
  • A. Melkumyan, F. Ramos
  • Published in IJCAI 2011
  • Computer Science
  • Multi-task learning remains a difficult yet important problem in machine learning. In Gaussian processes the main challenge is the definition of valid kernels (covariance functions) able to capture the relationships between different tasks. This paper presents a novel methodology to construct valid multi-task covariance functions (Mercer kernels) for Gaussian processes allowing for a combination of kernels with different forms. The method is based on Fourier analysis and is general for… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 21 REFERENCES
    Learning Multiple Tasks with Kernel Methods
    • 825
    • PDF
    Multi-task Gaussian Process Prediction
    • 719
    • PDF
    Dependent Gaussian Processes
    • 241
    • PDF
    A Sparse Covariance Function for Exact Gaussian Process Inference in Large Datasets
    • 63
    • PDF
    Sparse Gaussian Processes using Pseudo-inputs
    • 1,217
    • PDF
    Fast Sparse Gaussian Process Methods: The Informative Vector Machine
    • 507
    • PDF
    Semiparametric latent factor models
    • 205
    • PDF
    Multitask Learning
    • 611
    • PDF
    Using the Nyström Method to Speed Up Kernel Machines
    • 1,795