Computing with infinite networks

Advances in Neural Information Processing Systems

Volumn , Issue , 1997, Pages 295-301

  Williams, Christopher K I ^a  

^a ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION;

COVARIANCE FUNCTION; GAUSSIAN PROCESSES; GAUSSIANS; HIDDEN UNITS; INFINITE NUMBERS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84898974226     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley.
2. Hornik, K. (1993). Some new results on neural network approximation. Neural Networks 6 (8), 1069-1072.
3. Journel, A. G. and C. J. Huijbrcgts (1978). Mining Geostatistics. Academic Press.
4. Mac Kay, D. J. C. (1992). A Practical Bayesian Framework for Backpropagation Networks. Neural Computation 4(3), 448-472.
5. Neal, R. M. (1996). Bayesian Learning for Neural Networks. Springer. Lecture Notes in Statistics 118.
6. Parzen, E. (1962). Stochastic Processes. Holden-Day.
7. Poggio, T. and F. Girosi (1990). Networks for approximation and learning. Proceedings of IEEE 78, 1481-1497.
8. Wahba, G. (1990). Spline Models for Observational Data. Society for Industrial and Applied Mathematics. CBMS-NSF Regional Conference series in applied mathematics.
9. Whittle, P. (1963). Prediction and regulation by linear least-square methods. English Universities Press.
10. Williams, C. K. I. and C. E. Rasmussen (1996). Gaussian processes for regression. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8, pp. 514-520. MIT Press.