Computation with Infinite Neural Networks

Neural Computation

Volumn 10, Issue 5, 1998, Pages 1203-1216

  Williams, Christopher K I ^a  

^a ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000704059     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300017412     Document Type: Article

References (22)

1. Broomhead, D., & Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-355.
2. Castro, P. E., Lawton, W. H., & Sylvestre, E. A. (1986). Principal modes of variation for processes with continuous sample curves. Technometrics, 28(4), 329-337.
3. Cressie, N. A. C. (1993). Statistics for spatial data. New York: Wiley.
4. Gibbs, M., & MacKay, D. J. C. (1997). Efficient implementation of gaussian processes. Available from http://wol.ra.phy.cam.ac.uk/mackay/ homepage.html.
5. Girosi, F., Jones, M., & Poggio, T. (1995). Regularization theory and neural networks architectures. Neural Computation, 7(2), 219-269.
6. Hornik, K. (1993). Some new results on neural network approximation. Neural Networks, 6(8), 1069-1072.
7. Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. San Diego: Academic Press.
8. MacKay, D. J. C. (1992). A practical Bayesian framework for backpropagation networks. Neural Computation, 4(3), 448-472.
9. Matérn, B. (1980). Spatial variation (2nd ed.). Berlin: Springer-Verlag.
10. Moody, J. E., & Rögnvaldsson, T. S. (1997). Smoothing regularizers for projective basis functions. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9. Cambridge, MA: MIT Press.
11. Neal, R. M. (1996). Bayesian learning for neural networks. New York: Springer-Verlag.
12. Parzen, E. (1962). Stochastic processes. San Francisco: Holden-Day.
13. Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of IEEE, 78, 1481-1497.
14. Rasmussen, C. E. (1996). Evaluation of gaussian processes and other methods for non-linear regression. Unpublished Ph.D. dissertation, Department of Computer Science, University of Toronto. Available from http:// www.cs.utoronto.ca/~carl/.
15. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
16. Wahba, G. (1990). Spline models for observational data. Philadelphia: Society for Industrial and Applied Mathematics.
17. Wahba, G. (December, 1996). RBF's, SBF's, TreeBF's, Smoothing Spline ANOVA: Representers and pseudo-representers for a dictionary of basis functions for penalized likelihood estimates. Slides presented at the NIPS workshop on Model Complexity. Available from http://www.stat.wisc.edu/ ~wahba/wahba.html.
18. Whittle, P. (1963). Prediction and regulation by linear least-square methods. London: English Universities Press.
19. Williams, C. K. I. (1997). Computing with infinite networks. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9. Cambridge, MA: MIT Press.
20. 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 (pp. 514-520). Cambridge, MA: MIT Press.
21. Yaglom, A. M. (1987). Correlation theory of stationary and related random functions. Volume 1: Basic Results. Berlin: Springer-Verlag.
22. Zhu, H., Williams, C. K. I., Rohwer, R. J., & Morciniec, M. (1997). Gaussian regression and optimal finite dimensional linear models (Tech. Rep. No. NCRG/97/011). Aston University, UK. Available from http://www.ncrg. aston.ac.uk/Papers/.