Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming

@article{Kronberger2013EvolutionOC,
  title={Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming},
  author={G. Kronberger and Michael Kommenda},
  journal={ArXiv},
  year={2013},
  volume={abs/1305.3794}
}
In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared exponential covariance function is used as a default. However, this can lead to a misspecified model, which does not fit the data well. In the proposed approach we use a grammar… Expand
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