A matching pursuit approach to sparse Gaussian process regression

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 643-650

  Sathiya Keerthi, S ^a   Chu, Wei ^b  

^a YAHOO RESEARCH   (United States)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BASIS SELECTION; GAUSSIAN PROCESS REGRESSION; INFORMATION GAIN; MATCHING PURSUIT; PREDICTIVE DISTRIBUTIONS; REGRESSION MODEL;

REGRESSION ANALYSIS;

EID: 84864068501     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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3. Csató, L. and M. Opper. Sparse online Gaussian processes. Neural Computation, The MIT Press, 14:641-668, 2002.
4. Seeger,M. Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations. PhD thesis, University of Edinburgh, July 2003.
5. Seeger, M., C. K. I. Williams, and N. Lawrence. Fast forward selection to speed up sparse Gaussian process regression. In Workshop on AI and Statistics 9, 2003.
6. Smola, A. J. and P. Bartlett. Sparse greedy Gaussian process regression. In Leen, T. K., T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 619-625. MIT Press, 2001.
7. Vincent, P. and Y. Bengio. Kernel matching pursuit. Machine Learning, 48:165-187, 2002.
8. Williams, C. K. I. and M. Seeger. Using the Nyström method to speed up kernel machines. In Leen, T. K., T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 682-688. MIT Press, 2001.