The Boosting Approach to Machine Learning An Overview

By Robert E. Schapire. 2003

2,282 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Optimization & Training, Algorithms & Complexity

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Citations in the corpus

Boosting and Differential Privacy (2010, background): “To analyze the cumulative privacy loss, we obtain an O(ε2) bound on the expected privacy loss from a single ε-differentially private mechanism.”
BoostClean: Automated Error Detection and Repair for Machine Learning (2017, methodology): “This is the reason we applied a boosting-based approach towards selecting conditional re-pairs, described in the next section [46].”
Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “lusion of seasonal lags in our rolling window procedure allows the RNN to capture the remaining stochastic seasonality. This approach is inspired by the well-known \boosting" ensemble technique (Schapire, 2003), where STL deseasonalization can be seen as a weak base learner that is subsequently”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, neutral): “, decision trees, SVMs) [38].”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, methodology): “This approach is inspired by the well-known “boosting” ensemble technique (Schapire, 2003), where STL deseasonalization can be seen as a weak base learner that is subsequently supplemented by the RNN.”
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs (2020, methodology): “To simplify then-standard techniques, Rahimi & Recht (2009) show theoretically and empirically that learning linear combinations of random features can perform nearly as well as SVMs and Adaboost (Schapire, 2003).”
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning (2008, methodology): “Greedy algorithms for fitting networks of the form (2) have been analyzed, for example, in [7, 11, 9].”
A boosted classifier tree for hand shape detection (2004, methodology): “Boosting is a general method that can be used for improving the accuracy of a given learning algorithm [5].”
Vision based hand gesture recognition for human computer interaction: a survey (2012, methodology): “Boosting is a general method that can be used for improving the accuracy of a given learning algorithm (Schapire 2002).”
Discriminative learning of Markov random fields for segmentation of 3D scan data (2005, methodology): “[26] propose boosting random fields for image segmentation, combining ideas from boosting [22] and CRFs.”
Image Parsing: Unifying Segmentation, Detection, and Recognition (2003, positive): “,M} ( i = ±1), AdaBoost learning can be formulated as greedily optimizing the following function [44]”
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features (2017, background): “FATHMM-XF Supplementary 7 that gradient boosting (Schapire, 2003), random forest (Ho, 1995) extra trees (Geurts et al.”