Predictive approaches for choosing hyperparameters in gaussian processes

Neural Computation

Volumn 13, Issue 5, 2001, Pages 1103-1118

  Sundararajan, S ^a   Keerthi, S S ^b  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; NORMAL DISTRIBUTION; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY; STATISTICAL MODEL;

LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 0035344742     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/08997660151134343     Document Type: Article

References (18)

1. Breiman, L. (1995). Better subset regression using the non-negative garrote. Technometrics, 37(4), 373-384.
2. Burman, P. (1989). A comparative study of ordinary cross-validation and the repeated learning testing methods. Biometrika, 76(3), 503-514.
3. Craven, P., & Wahba, G. (1979). Smoothing noisy data with spline functions. Numer. Math., 31, 377-403.
4. Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the jackknife and cross-validation. American Statistician, 37(1), 36-48.
5. Friedman, J. H. (1991). Multivariate adaptive regression splines. Ann. Stat., 19, 1-141.
6. Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70, 320-328.
7. Geisser, S., & Eddy, W. F. (1979). A predictive approach to model selection, Journal of the American Statistical Association, 74, 153-160.
8. Golub, G.H., & Van Loan, C. F. (1985). Matrix computations. Baltimore: John Hopkins University Press.
9. MacKay D. J. C. (1997). Gaussian processes - A replacement for neural networks Available online at: http://www.wol.ra.phy.cam.ac.uk/mackay/.
10. Neal, R. M. (1996). Bayesian learning for neural networks. New York: Springer-Verlag.
11. Neal, R. M. (1997). Monte Carlo implementation of gaussian process models for Bayesian regression and classification (Tech. Rep. No. 9702). Toronto: Department of Statistics, University of Toronto.
12. Rao, C. R. (1991). Linear Statistical inference and its applications. New Delhi: Wiley Eastern Limited.
13. Rasmussen, C. (1996). Evaluation of gaussian processes and other methods for non-linear regression. Unpublished doctoral dissertation, University of Toronto.
14. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion). J. R. Statist. Soc. B, 36, 111-147.
15. Sundararajan, S. (1999). Predictive approaches for choosing hyperparameters in linear and nonlinear regression problems. Unpublished doctoral dissertation, Indian Institute of Science, Bangalore, India.
16. Sundararajan, S., & Keerthi, S. S. (1999a). Predictive approaches for choosing hyperparameters in artificial neural networks. Submitted.
17. Sundararajan, S., & Keerthi, S. S. (1999b). Predictive approaches for choosing hyperparameters in gaussian processes. In S. A. Solla, T. K. Leen, & K-R. Müller (Eds.), Advances in neural information processing systems, 12. Cambridge, MA: MIT Press.
18. Williams, C. K. I., & Rasmussen, C. E. (1996). Gaussian processes for regression. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems, 8. Cambridge, MA: MIT Press.