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Abstract: The Bayesian committee machine (BCM) is a novel approach to combining estimators which were trained on di erent data sets. Although the BCM can be applied to the combination of any kind of estimators the main foci are Gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data. Somewhat surprisingly, we nd that the performance of the BCM improves if several test points are... (Update)
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BibTeX entry: (Update)
Tresp, V., The Bayesian committee machine, Neural Computation, 12, 2000. http://citeseer.ist.psu.edu/tresp00bayesian.html More
@article{ tresp00bayesian,
author = "Volker Tresp",
title = "A Bayesian Committee Machine",
journal = "Neural Computation",
volume = "12",
number = "11",
pages = "2719-2741",
year = "2000",
url = "citeseer.ist.psu.edu/tresp00bayesian.html" }
Citations (may not include all citations):1291 The nature of statistical learning theory (context) - Vapnik - 1995 ACM
422 Networks for approximation and learning (context) - Poggio, Girosi - 1990
340 Bayesian Theory (context) - Bernardo, Smith - 1994
269 Bayesian learning for neural networks (context) - Neal - 1996 ACM
149 Graphical Models in Applied Multivariate Statistics (context) - Whittaker - 1990
109 An equivalence between sparse approximation and support vect.. - Girosi - 1998 ACM DBLP
85 Prediction with Gaussian processes: from linear regression t.. - Williams
78 Gaussian processes for regression - Williams, Rasmussen - 1996
71 The connection between regularization operators and support .. - Smola, Sch et al. - 1998 ACM
53 Evaluation of Gaussian processes and other methods for non-l.. - Rasmussen - 1996 ACM
47 Monte Carlo implementation of Gaussian process models for Ba.. - Neal - 1997
46 Combining estimators using non-constant weighting functions - Tresp, Taniguchi - 1995 DBLP
45 Advances in neural information processing systems (context) - Hanson, Lippmann - 1998
21 A sparse representation for function approximation - Poggio, Girosi - 1998 ACM DBLP
17 Bayesian model comparison and backprop nets - MacKay - 1992 DBLP
16 Bayesian numerical analysis (context) - Skilling - 1993
15 Averaging regularized estimators - Taniguchi, Tresp - 1997 ACM DBLP
7 Smoothing regularizers for projective basis function network.. - Moody, ognvaldsson - 1997 ACM DBLP
7 Regression Analysis (context) - Sen, Srivastava - 1990 ACM
6 ective number of parameters: an analysis of generalization a.. (context) - Moody, The - 1992
5 Spline models for observational data (context) - Stat, Ser et al. - 1990
4 Ecient implementation of Gaussian processes (context) - Gibbs, MacKay - 1997
3 the cross-validated smoothing spline (context) - Wahba - 1983
3 Finite-dimensional approximation of Gaussian processes - Ferrari-Trecate, Williams et al. - 1999 ACM DBLP
2 Generalized additive models (context) - Computation, No et al. - 1990
2 Gaussian regression and optimal nite dimensional linear mode.. (context) - pp, Zhu et al. - 1998
1 Computing with innite neural networks (context) - Williams
1 Bayesian regression lters and the issue of priors (context) - Zhu - 1996
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