Stacked regressions

By Leo Breiman. 2004

1,394 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Nature and Landscape Conservation, Statistics and Probability

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Citations in the corpus

Ensemble Methods for Causal Effects in Panel Data Settings (2019, methodology): “Alternatives to this stacked regression method (Leo Breiman, 1996) include ridge regression (Mark van der Laan, Eric Polley, and Alan Hubbard, 2007), and Bayesian model averaging (Jennifer Hoeting, David Madigan, Adrian Raftery, and Chris Volinsky, 1999).”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, neutral): “In stacking, a linear combination of individual predictors is used to achieve better accuracy (Breiman, 1996).”
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors (2020, methodology): “Stacking (Wolpert, 1992; Breiman, 1996; LeBlanc and Tibshirani, 1996; Clarke, 2003) and its Bayesian variants (Clyde and Iversen, 2013; Le and Clarke, 2017; Yao et al., 2018a, 2021) are model averaging techniques for combining a discrete set of ﬁtted models in the setting where we have data y = ( y…”
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be? (2013, methodology): “Ensemble methods such as Bagging (Breiman, 1996a), Boosting (Demiriz et al., 2002), Stacking (Wolpert, 1992; Breiman, 1996b; Sill et al., 2009), and Bayesian Model Averaging (Hoeting et al., 1999) are also commonly employed to exact every last drop of accuracy from a given set of algorithmic…”
UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022 (2022, positive): “Specifically, we use the stacking method [15, 16] illustrated in Fig.”
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (2017, methodology): “The methods include linear averaging, bagging, boosting, stacked regression [29] etc.”
Pushing the Limits of Translation Quality Estimation (2017, background): “Stacking architectures (Wolpert, 1992; Breiman, 1996) have proved effective in structured NLP problems (Cohen and de Carvalho, 2005; Martins et al., 2008).”
Using Supervised Learning to Improve Monte Carlo Integral Estimation (2011, background): “Interested readers can see stacking applied to improving regression [15], probability density estimation [16], confidence interval estimation [17], and optimization [18].”
Genomic Selection in Plant Breeding: A Comparison of Models (2012, methodology): “The approach of stacked regression described by Breiman (1996a) was also used to build a combined predictor.”
Computationally efficient whole-genome regression for quantitative and binary traits (2020, methodology): “22).”
Ensemble methods and partial least squares regression (2004, background): “The bagged prediction is then the average (for regressions) or majority vote (for classifications) of the individual predictions.”
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification (2017, methodology): “Breiman [4] also studied stacked regression by using cross-validation to construct the ‘good’ combination.”
Stacked Generalization: An Introduction to Super Learning (2017, positive): “Later, Breiman demonstrated how stacking can be used to improve the predictive accuracy in a regression context, and showed that imposing certain constraints on the higher-level model improved predictive performance [2].”
Importance Sampled Learning Ensembles (2003, methodology): “Bagging (Breiman 1996a) is an elegantly simple ensemble learning method.”
Ensemble deep learning: A review (2021, methodology): “To obtain the good linear combination of the base learners in regression, cross-validation data and least squares under non-negativity constraints was used to get the optimal weights of combination [127].”
A theoretical and experimental analysis of linear combiners for multiple classifier systems (2005, methodology): “One regularization method suggested for avoiding this problem is to constrain the weights to be nonnegative [17], [11], [4], [13].”