The generalized Bayesian committee machine

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The generalized Bayesian committee machine

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Boston, Massachusetts, United States

Pages: 130 - 139

Year of Publication: 2000

ISBN:1-58113-233-6

Author

Volker Tresp

Siemens AG, Corporate Technology, Otto-Hahn-Ring 6, 81730 München, Germany

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 4, Downloads (12 Months): 26, Citation Count: 1

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	J. M. Bernando and A. F. M. Smith. Bayesian Theory. aotm Wiley and Sons, New York, 1994.
	2	L. Csato, E. Fokou, M. Opper, B. Schottky, and O. VVTinther. Efficient approaches to gaussian process classification. In S. A. Solla, T. K. Leen, and K.-R. Mfiller, 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	Yoav Freund, Boosting a weak learning algorithm by majority, Information and Computation, v.121 n.2, p.256-285, Sept. 1995 [doi>10.1006/inco.1995.1136]
	6	Yoav Freund , Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, v.55 n.1, p.119-139, Aug. 1997 [doi>10.1006/jcss.1997.1504]
	7	J. Ffiedmma, T. Hastie, mad R. Tibstfirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 2000. To appear.
	8	T. J. Hastie mad R. J. Tibstfirani. Generalized additive models. Chapman g Hall, 1990.
	9	R. A. Jacobs, M. I. Jordan, S. J. Nowlma, mad J. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3, 1991.
	10	D. J. C. MacKay. Bayesian model comparison mad backprop nets. In J. E. Moody, S. J. Hmason, mad R. P. Lippmarm, editors, Advance.s in neural information proee.s.sing .sy.stem.s 4, pages 839-846. Morgan Kaufinama, 1992.
	11	D. J. C. MacKay. Introduction to gaussima processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, volume 168. NATO Asi Series F, Computer mad Systems Sciences, Morgan Kaufinama, 1998.
	12	P. McCullagh mad J. Nelder. Generalized linear models. Chapman Hall, 1983.
	13	J. E. Moody. The effective number of parameters: an analysis of generalization mad regulmization in nonlinear lemming systems. In J. E. Moody, S. J. Hmason, mad R. P. Lippmama, editors, Advances in neural information processing systems 4, pages 847-854. Morgan Kaufinama, 1992.
	14	Radford M. Neal, Bayesian Learning for Neural Networks, Springer-Verlag New York, Inc., Secaucus, NJ, 1996
	15	R. M. Neal. Monte Carlo implementation of Gaussian process models for Baye.sian regression and classification. Chapman g Hall, 1997.
	16	J. C. Platt. Probabilistic outputs for support vector maclfines mad comparisons to regularized fikefihood methods. In A. J. Smola, editor, Advance.s in large Margin Classifiers. MIT press, 2000.
	17	T. Poggio mad F. Girosi. Networks fox" approximation mad learning. Proceedings of the IEEE, 78:1481-1497, 1990.
	18	C. E. Rasmussen. Evaluation of gaussima processes mad other methods for non-finem" regression. Teclmieal report, Department of Computer Science, University of Toronto, 1996.
	19	Robert E. Schapire, The Strength of Weak Learnability, Machine Learning, v.5 n.2, p.197-227, Jun. 1990 [doi>10.1023/A:1022648800760]
	20	Volker Tresp, A Bayesian Committee Machine, Neural Computation, v.12 n.11, p.2719-2741, November 2000 [doi>10.1162/089976600300014908]
	21	V. Tresp. Interacting gaussima processes for graptfieal models, submitted, 2000.
	22	V. Tresp mad M. Tmfiguclfi. Combining estimators using non-eonstmat weighting functions. In G. Tesauro, D. S. Touretzky, mad T. K. Leen, editors, Advance.s in neural information procssing systems7, pages 419-426. MIT Press, 1995.
	23	Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
	24	G. Yahba. Spline models for observational data. Society for Industrial mad Appfied Mathematics, 1983.
	25	G. Yahba. Bayesian "confidence intervals" for the cross-validated smoottfing spfine. J. Roy. Stat. Soc. Set. B, 10:133-150, 1990.
	26	Grace Wahba, Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
	27	Christopher K. I. Williams, Computation with infinite neural networks, Neural Computation, v.10 n.5, p.1203-1216, July 1, 1998 [doi>10.1162/089976698300017412]
	28	Christopher K. I. Williams , David Barber, Bayesian Classification With Gaussian Processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.20 n.12, p.1342-1351, December 1998 [doi>10.1109/34.735807]
	29	C. K. I. Williams mad C. E. Rasmussen. Gaussima processes for regression. In M. C. Mozer mad M. E. Hasselmo, editors, Advanee.s in neural information processing systems8, pages 514-520. MIT Press, 1996.