# Multiple Gaussian Process Models

@article{Archambeau2011MultipleGP, title={Multiple Gaussian Process Models}, author={C. Archambeau and Francis R. Bach}, journal={arXiv: Machine Learning}, year={2011} }

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…

## 5 Citations

Regularization Strategies and Empirical Bayesian Learning for MKL

- Computer ScienceArXiv
- 2010

This paper shows how different MKL algorithms can be understood as applications of either regularization on the kernel weights or block-norm-based regularization, which is more common in structured sparsity and multi-task learning.

Upgrading from Gaussian Processes to Student's-T Processes

- Computer Science
- 2018

The Student's-T distribution has higher Kurtosis than a Gaussian distribution and so outliers are much more likely, and the posterior variance increases or decreases depending on the variance of observed data sample values.

Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data

- Computer ScienceArXiv
- 2017

The research results demonstrate that the Gaussian Process models are competitive with or better than the well-known Support Vector Machine in terms of classification performance even in the cases of single kernel learning.

Efficient global optimization with ensemble and selection of kernel functions for engineering design

- Computer ScienceStructural and Multidisciplinary Optimization
- 2018

It is revealed that the ensemble techniques improve the robustness and performance of EGO and that the use of Matérn-kernels yields better results than those of the Gaussian kernel when EGO with a single kernel is considered.

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