Please use this identifier to cite or link to this item: https://doi.org/10.1162/08997660151134343
Title: Predictive approaches for choosing hyperparameters in gaussian processes
Authors: Sundararajan, S.
Keerthi, S.S. 
Issue Date: May-2001
Source: Sundararajan, S., Keerthi, S.S. (2001-05). Predictive approaches for choosing hyperparameters in gaussian processes. Neural Computation 13 (5) : 1103-1118. ScholarBank@NUS Repository. https://doi.org/10.1162/08997660151134343
Abstract: 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 (CV) 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.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/61140
ISSN: 08997667
DOI: 10.1162/08997660151134343
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