Discovering hidden features with Gaussian processes regression

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 613-619

  Vivarelli, Francesco ^a   Williams, Christopher K I ^b  

^a Centro Ricerche Ambientali Montecatini   (Italy)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

DIAGONAL MATRICES; EIGENANALYSIS; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES REGRESSIONS; POSITIVE-DEFINITE MATRICES; TEST PROBLEM; TRAINING DATA;

COVARIANCE MATRIX;

EID: 84869164437     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Breiman, L. (1993). Hinging hyperplanes for regression, classification and function approximation. IEEE Trans, on Information Theory, 39(3):999-1013.
2. Girosi, F., Jones, M., and Poggio, T. (1995). Regularization Theory and Neural Networks Architectures. Neural Computation, 7(2):219-269.
3. Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Chapman and Hall, London.
4. Neal, R. M. (1996). Bayesian Learning for Neural Networks. Springer. Lecture Notes in Statistics 118.
5. Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. (1992). Numerical Recipes in C. The Art of Scientific Computing. Cambridge University Press, second edition.
6. Rasmussen, C. E. (1996). Evaluation of Gaussian Processes and Other Methods for Nonlinear Regression. PhD thesis, Dept. of Computer Science, University of Toronto.
7. Whittle, P. (1963). Prediction and regulation by linear least square methods. English Universities Press.
8. Williams, C. K. I. and Rasmussen, C. E. (1996). Gaussian processes for regression. In Touretzky, M. C. and Mozer, M. C. and Hasselmo, M. E., editors, Advances in Neural Information Processing Systems 8, pages 514-520. MIT Press.
9. Williams, P. M. (1996). Conditional multivariate densities. Neural Computation, 8(4).