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Abstract: In this paper we introduce the Generalized Bayesian Committee Machine (GBCM) for applications with large data sets. In particular, the GBCM can be used in the context of kernel based systems such as smoothing splines, kriging, regularization networks and Gaussian process regression which |for computational reasons| are otherwise limited to rather small data sets. The GBCM provides a novel and principled way of combining estimators trained for regression, classi cation, the prediction of ... (Update)
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.... an overview of the state of the art of the respective topics, this section almost exclusively presents recent results of the author [38, 39]. 4.1 Theoretical Foundations Let x be a vector of input variables and let y be the output variable. We assume that, given a...
...outputs. In this way, the degree of overlap between classes can be in uenced by varying 2 . This is the same data set as used in [7], see the reference for a detailed description. The CHECKER data set is sampled uniformly from a 4 4 checkerboard grid on [0; 1] 0; 1]...
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BibTeX entry: (Update)
Tresp, V. (2000). The Generalized Bayesian Committee Machine. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2000. http://citeseer.ist.psu.edu/tresp00generalized.html More
@inproceedings{ tresp00generalized,
author = "Volker Tresp",
title = "The generalized Bayesian committee machine",
booktitle = "Knowledge Discovery and Data Mining",
pages = "130-139",
year = "2000",
url = "citeseer.ist.psu.edu/tresp00generalized.html" }
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450 Generalized additive models - Hastie, Tibshirani - 1990
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413 Adaptive mixtures of local experts (context) - Jacobs, Jordan et al. - 1991
340 Bayesian Theory (context) - Bernardo, Smith - 1994
269 Bayesian learning for neural networks (context) - Neal - 1996
236 Additive logistic regression: a statistical view of boosting - Friedman, Hastie et al. - 2000
180 Boosting a weak learning algorithm by majority - Freund - 1995
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59 A decision theoretic generalization of online learning and a.. (context) - Freund, Schapire - 1997
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32 Introduction to gaussian processes (context) - MacKay - 1998
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6 ective number of parameters: an analysis of generalization a.. (context) - Moody - 1992
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