The generalized Bayesian committee machine

Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2000, Pages 130-139

  Tresp, Volker ^a  

^a SIEMENS AG   (Germany)

Author keywords

Combining Estimators; Committee Machines; Data Mining; Gaussian Processes; Kernel Based Systems; Support Vector Machines

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; PROBABILITY; QUERY LANGUAGES; REGRESSION ANALYSIS;

SUPPORT VECTOR MACHINES;

DATA MINING;

EID: 0034593067     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/347090.347118     Document Type: Conference Paper

References (29)

1. J. M. Bernardo and A. F. M. Smith. Bayesian Theory. John Wiley and Sons, New York, 1994.
2. L. Csató, E. Fokoué, M. Opper, B. Schottky, and O. Winther. Efficient approaches to gaussian process classification. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in neural information processing systems 12. MIT Press, 2000.
3. H. Drucker. Boosting neural networks. In A. J. C. Sharkey, editor, Combining artificial neural nets. Springer, 1999.
4. L. Fahrmeir and G. Tutz. Multivariate Statistical Modeling Based on Generalized Linear Models. Springer, New York, 1994.
5. Y. Freund. Boosting a weak learning algorithm by majority. Information and Computation, 121(2):256-285, 1995.
6. Y. Freund and R. E. Schapire. A decision theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55, 1997.
7. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 2000. To appear.
8. T. J. Hastie and R. J. Tibshirani. Generalized additive models. Chapman & Hall, 1990.
9. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and J. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3, 1991.
10. D. J. C. MacKay. Bayesian model comparison and backprop nets. In J. E. Moody, S. J. Hanson, and R. P. Lippmann, editors, Advances in neural information processing systems 4, pages 839-846. Morgan Kaufmann, 1992.
11. D. J. C. MacKay. Introduction to gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, volume 168. NATO Asi Series F, Computer and Systems Sciences, Morgan Kaufmann, 1998.
12. P. McCullagh and J. Nelder. Generalized linear models. Chapman & Hall, 1983.
13. J. E. Moody. The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. In J. E. Moody, S. J. Hanson, and R. P. Lippmann, editors, Advances in neural information processing systems 4, pages 847-854. Morgan Kaufmann, 1992.
14. R. M. Neal. Bayesian learning for neural networks. Springer, 1996.
15. R. M. Neal. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Chapman & Hall, 1997.
16. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Stools, editor, Advances in large Margin Classifiers. MIT press, 2000.
17. T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of the IEEE, 78:1481-1497, 1990.
18. C. E. Rasmussen. Evaluation of gaussian processes and other methods for non-linear regression. Technical report, Department of Computer Science, University of Toronto, 1996.
19. R. E. Schapire. The strength of weak learnability. Machine learning, 5(2):197-227, 1990.
20. V. Tresp. A bayesian committee machine. Neural Computation, 2000. In print.
21. V. Tresp. Interacting gaussian processes for graphical models. submitted, 2000.
22. V. Tresp and M. Taniguchi. Combining estimators using non-constant weighting functions. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in neural information processing systems 7, pages 419-426. MIT Press, 1995.
23. V. N. Vapnik. The nature of statistical learning theory. Springer, New York, 1995.
24. G. Wahba. Spline models for observational data. Society for Industrial and Applied Mathematics, 1983.
25. G. Wahba. Bayesian "confidence intervals" for the cross-validated smoothing spline. J. Roy. Stat. Soc. Ser. B, 10:133-150, 1990.
26. G. Wahba. Support vector machines, reproducing kernel hilbert spaces and randomized GACV. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods, pages 69-88. MIT Press, 1999.
27. C. K. I. Williams. Computing with infinite neural networks. Neural Computation, 10:1203-1216, 1998.
28. C. K. I. Williams and D. Barber. Bayesian classification with gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:1342-1351, 1998.
29. C. K. I. Williams and C. E. Rasmussen. Gaussian processes for regression. In M. C. Mozer and M. E. Hasselmo, editors, Advances in neural information processing systems 8, pages 514-520. MIT Press, 1996.