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Approximation Methods for Gaussian Process Regression


Book Chapter


A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

Author(s): Quiñonero-Candela, J. and Rasmussen, CE. and Williams, CKI.
Book Title: Large-Scale Kernel Machines
Pages: 203-223
Year: 2007
Month: September
Day: 0

Series: Neural Information Processing
Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Approximation Methods for Gaussian Process Regression},
  author = {Quiñonero-Candela, J. and Rasmussen, CE. and Williams, CKI.},
  booktitle = {Large-Scale Kernel Machines},
  pages = {203-223},
  series = {Neural Information Processing},
  editors = {Bottou, L. , O. Chapelle, D. DeCoste, J. Weston},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2007},
  month_numeric = {9}