Please use this identifier to cite or link to this item:
|Title:||Predictive approaches for choosing hyperparametrs in Gaussian Processes||Authors:||Sundararajan, S.
Sathiya Keerthi, S.
|Issue Date:||2000||Citation:||Sundararajan, S., Sathiya Keerthi, S. (2000). Predictive approaches for choosing hyperparametrs in Gaussian Processes. Advances in Neural Information Processing Systems : 631-637. ScholarBank@NUS Repository.||Abstract:||Gaussian Processes are powerful regression models specified by parametrized mean and covariance functions. Standard approaches to estimate these parameters (known by the name Hyperparam-eters) are Maximum Likelihood (ML) and Maximum APosterior (MAP) approaches. In this paper, we propose and investigate predictive approaches, namely, maximization of Geisser's Surrogate Predictive Probability (GPP) and minimization of mean square error with respect to GPP (referred to as Geisser's Predictive mean square Error (GPE)) to estimate the hyperparameters. We also derive results for the standard Cross-Validation (CV) error and make a comparison. These approaches are tested on a number of problems and experimental results show that these approaches are strongly competitive to existing approaches.||Source Title:||Advances in Neural Information Processing Systems||URI:||http://scholarbank.nus.edu.sg/handle/10635/129923||ISBN:||0262194503||ISSN:||10495258|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 11, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.