Sundararajan, S and Keerthi, SS (2001) Predictive approaches for choosing hyperparameters in Gaussian processes. In: Neural Computation, 13 (5). pp. 1103-1118.
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Gaussian processes are powerful regression models specified by parameterized mean and covariance functions. Standard approaches to choose these parameters (known by the name hyperparameters) are maximum likelihood and maximum a posteriori. In this article, we propose and investigate predictive approaches based on Geisser's predictive sample reuse (PSR) methodology and the related Stone's cross-validation ICV) methodology. More specifically, we derive results for Geisser's surrogate predictive probability (GPP), Geisser's predictive mean square error (GPE), and the standard CV error and make a comparative study. Within an approximation we arrive at the generalized cross-validation (GCV) and establish its relationship with the GPP and GPE approaches. These approaches are tested on a number of problems. Experimental results show that these approaches are strongly competitive with the existing approaches.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to M I T Press.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||10 Feb 2010 07:20|
|Last Modified:||19 Sep 2010 04:56|
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