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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548397.0, MPI für biologische Kybernetik / Biologische Kybernetik
Sparse Spectrum Gaussian Process Regression
Authors:Lázaro-Gredilla, M.; Quinonero-Candela, J.; Rasmussen, C.E.; Figueiras-Vidal, A.R.
Date of Publication (YYYY-MM-DD):2010-06
Title of Journal:Journal of Machine Learning Research
Volume:11
Start Page:1865
End Page:1881
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations. We discuss both the weight space and function space representations, and note that the new construction implies priors over functions which are always stationary, and can approximate any covariance function in this class.
External Publication Status:published
Document Type:Article
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6664
URL:http://www.jmlr.org/papers/volume11/lazaro-gredill...
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