A Bayesian committee machine

Neural Computation

Volumn 12, Issue 11, 2000, Pages 2719-2741

  Tresp, Volker ^a  

^a SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DIABETES MELLITUS; HUMAN; NORMAL DISTRIBUTION; ONLINE SYSTEM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DIABETES MELLITUS; HUMANS; NORMAL DISTRIBUTION; ONLINE SYSTEMS;

EID: 0034320395     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300014908     Document Type: Article

References (29)

1. Bernardo, J., M., & Smith, A. F. M. (1994). Bayesian theory. New York: Wiley.
2. Ferrari-Trecate, G., Williams, C. K. I., & Opper, M. (1999). Finite-dimensional approximation of gaussian processes. In M. S. Kearns, S. A. Solla, & D. A. Cohn (Eds.), Advances in neural information processing systems, 11 (218-224). Cambridge MA: MIT Press.
3. Gibbs, M., & MacKay, D. J. C. (1997). Efficient implementation of gaussian processes. Available online at http://wol.ra.phy.cam.ac.uk/mackay/home-page.html.
4. Girosi, F. (1998). An equivalence between sparse approximation and support vector machines. Neural Computation, 10(6), 1454-1480.
5. Hastie, T., & Tibshirani, R. J. (1990). Generalized additive models. London: Chapman & Hall.
6. MacKay, D. J. C. (1992). Bayesian model comparison and backprop nets. In J. E. Moody, S. J. Hanson, & R. P. Lippmann (Eds.), Advances in neural information processing systems, 4 (839-846). San Mateo, CA: Morgan Kaufmann.
7. MacKay, D. J. C. (1998). Introduction to gaussian processes. In C. Bishop (Ed.), Neural networks and machine learning. Berlin: Springer-Verlag.
8. Moody, J. E. (1992). The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems. In J. E. Moody, S. J. Hanson, & R. P. Lippmann (Eds.), Advances in neural information processing systems, 4 (pp. 847-854). San Mateo, CA: Morgan Kaufmann.
9. Moody, J. E., & Rögnvaldsson, T. S. (1997). Smoothing regularizers for projective basis function networks. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9. Cambridge, MA: MIT Press.
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. Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE, 78, 1481-1497.
13. Poggio, T., & Girosi, F. (1998). A sparse representation for function approximation. Neural Computation, 10(6), 1445-11454.
14. Rasmussen, C. E. (1996). Evaluation of gaussian processes and other methods for nonlinear regression. Unpublished doctoral dissertation, University of Toronto. Available online at http://www.cs.utoronto.ca/∼carl/.
15. Sen, A., & Srivastava, M. (1990). Regression analysis. New York: Springer-Verlag.
16. Skilling, J. (1993). Bayesian numerical analysis. In W. T., Jr. Grandy, & P. Milonni (Eds.), Physics and probability. Cambridge: Cambridge University Press.
17. Smola, A. J., Schölkopf, B., & Müller, K.-R. (1998). The connection between regularization operators and support vector kernels. Neural Networks, 11, 637-649.
18. Taniguchi, M., & Tresp, V. (1997). Averaging regularized estimators. Neural Computation, 9(5), 1163-1178.
19. Tresp, V., & Taniguchi, M. (1995). Combining estimators using non-constant weighting functions. In G. Tesauro, D. S. Touretzky, & T. K. Leen (Eds.), Advances in neural information processing systems, 7 (pp. 419-426). Cambridge, MA: MIT Press.
20. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
21. Wahba, G. (1990). Spline models for observational data. Philadelphia: Society for Industrial and Applied Mathematics.
22. Wahba, G. (1983). Bayesian "confidence intervals" for the cross-validated smoothing spline. J. Roy. Stat. Soc. Ser. B, 45(1), 133-150.
23. Whittaker, J. (1990). Graphical models in applied multivariate statistics. New York: Wiley.
24. 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.
25. Williams, C. K. I. (1998a). Computing with infinite neural networks. Neural Computation, 10, 1203-1216.
26. Williams, C. K. I. (1998b). Prediction with gaussian processes: From linear regression to linear prediction and beyond. In M. I. Jordan (Ed.), Learning in graphical models (pp. 599-621). Boston: Kluwer.
27. 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.
28. Zhu, H., & Rohwer (1996). Bayesian regression filters and the issue of priors. Neural Comp. Appl., 4, 130-142.
29. Zhu, H., Williams, C. K. I., Rohwer, R., & Morciniec, M. (1998). Gaussian regression and optimal finite dimensional linear models. In C. M. Bishop (Ed.), Neural networks and machine learning (315-332). Berlin: Springer-Verlag.