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In regression tasks the distribution of the data is often too complex to be fitted by a single model.
Induction of decision trees
Quinlan, J. R · 1986
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Adaptive mixtures of local experts
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Fitting a mixture model by expectation maximization to discover motifs in bipolymers
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Hierarchical mixtures of experts and the em algorithm
Jordan, M. I. and Jacobs, R. A · 1994
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Waterhouse, S. and Robinson, A · 1995
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Modeling of strength of high-performance concrete using artificial neural networks
Yeh, I.-C · 1998
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Error bounds for functional approximation and estimation using mixtures of experts
Zeevi, A. J., Meir, R., and Maiorov, V · 1998
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Boosted mixture of experts: an ensemble learning scheme
Avnimelech, R. and Intrator, N · 1999
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On the asymptotic normality of hierarchical mixtures-of-experts for generalized linear models
Jiang, W. and Tanner, M. A · 2000
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Random forests
Breiman, L · 2001
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Mixtures of gaussian processes
Tresp, V · 2001
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Bayesian hierarchical mixtures of experts
Bishop, C. M. and Svenskn, M · 2002
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Classification and regression by randomforest
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Infinite mixtures of gaussian process experts
Rasmussen, C. E. and Ghahramani, Z · 2002
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Support vector machines experts for time series forecasting
Cao, L · 2003
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Fast forward selection to speed up sparse gaussian process regression
Seeger, M., Williams, C., and Lawrence, N · 2003
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Regression diagnostics: Identifying influential data and sources of collinearity , volume 571
Belsley, D. A., Kuh, E., and Welsch, R. E · 2005
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Learning ambiguities using bayesian mixture of experts
Kanaujia, A. and Metaxas, D · 2006
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API design for machine learning software: experiences from the scikit-learn project
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., and Varoquaux, G · 2013
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Fast allocation of gaussian process experts
Nguyen, T. and Bonilla, E · 2014
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Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
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Distributed gaussian processes
Deisenroth, M. P. and Ng, J. W · 2015
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UCI machine learning repository, 2017
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Learning deep mixtures of gaussian process experts using sum-product networks
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