Filtered Gaussian processes for learning with large data-sets

Lecture Notes in Computer Science

Volumn 3355, Issue , 2005, Pages 128-139

  Shi, Jian Qing ^a   Murray Smith, Roderick ^b,c   Titterington, D Mike ^b   Pearlmutter, Barak A ^c  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Filtering transformation; Gaussian process regression model; Karhunen Loeve expansion; Kernel based non parametric models; Principal component analysis

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA REDUCTION; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; REGRESSION ANALYSIS;

FILTERING TRANSFORMATION; GAUSSIAN PROCESS REGRESSION MODEL; KARHUNEN-LOEVE EXPANSION; KERNEL-BASED NON-PARAMETRIC MODELS;

LEARNING SYSTEMS;

EID: 24144438211     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30560-6_5     Document Type: Conference Paper

References (13)

1. Gibbs, Mark and MacKay, D. J. C. (1997). Efficient implementation of Gaussian processes.
2. Luo, Z and Wahba, G. (1997). Hybrid adaptive splines. J. Amer. Statist. Assoc., 92, 107-116.
3. Murray-Smith, R. and Pearlmutter, B. A. (2003). Transformations of Gaussian process priors. TR-2003-149, Department of Computing Science, University of Glasgow, Scotland, June.
4. O'Hagan, A. (1978). On curve fitting and optimal design for regression (with discussion). Journal of the Royal Statistical Society B, 40, 1-42.
5. Poggio, T. and Girosi, F. (1990). Networks for approximation and learning. Proceedings of IEEE, 78, 1481-1497.
6. Seeger, M., Willians, C. K. I., and Lawrence, N. D. (2003). Fast forward selection to speed up sparse Gaussian process regression. In Bishop, C. M. and Frey, B. J., editors, Proceedings of the Ninth International Workshop on AI and Statistics.
7. Rasmussen, C. E. (1996). Evaluation of Gaussian Processes and Other Methods for Non-linear Regression. PhD Thesis. University of Toronto. (Available from http: //bayes.imm.dtu.dk)
8. Shi, J. Q., Murray-Smith, R. and Titterington, D. M. (2002). Hierarchical Gaussian Process Mixtures for Regression, DCS Technical Report TR-2002-107/Dept. Statistics Tech. Report 02-7, University of Glasgow, Scotland.
9. Tresp, V. (2000). The Bayesian committee machine. Neural Computation, 12, 27192741.
10. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer Verlag, New York.
11. Wahba, G. (1990). Spline Models for Observational Data. SIAM, Philadelphia, PA. CBMS_NSF Regional Conference series in applied mathematics.
12. Williams, C. K. I. and Rasmussen, C. E. (1996). Gaussian process for regression, in D.S. Touretzky et al (eds), Advances in Neural Information Processing Systems 8, MIT Press.
13. Williams, C. K. I. and Seeger, M. (2001). Using the Nyström method to speed up kernel machines. Advances in Neural Information Processing Systems, 13. Eds T. K. Leen, T. G. Diettrich and V. Tresp. MIT Press.