• Corpus ID: 88511900

Multiple Gaussian Process Models

  title={Multiple Gaussian Process Models},
  author={C. Archambeau and Francis R. Bach},
  journal={arXiv: Machine Learning},
We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices that best explains the data and by doing so improve the generalisation on unseen data. Sparsity in the kernel weights is obtained by adopting a hierarchical Bayesian approach: Gaussian process priors are imposed over the latent functions and generalised inverse Gaussians on their associated weights. This construction is equivalent to imposing a… 

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