An Introduction to Boosting and Leveraging

By R. Meir, Gunnar Rätsch. 2002

523 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition (2016, methodology): “By using the update rule (6) in the Lipschitz condition (5) we have By taking the expectation of both sides with respect to i k we have By using the PL inequality (3) and subtracting f ∗ from both sides, we get Applying this recursively and using iterated expectations yields the result.”
Prototype Classification: Insights from Machine Learning (2009, background): “The problem to be solved in the boosting scenario is the maximization of the smallest margin in the training sample (Schapire, Freund, Bartlett, & Lee, 1998): (5.5) It is known that the solution of this problem is a convex combination of elements of H (R¨atsch & Meir, 2003).”
On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms (2010, background): “For an overview see for example [FS99, Sch03, MR03 ].”
Different Paradigms for Choosing Sequential Reweighting Algorithms (2004, methodology): “…devoted to ensemble methods has given particular attention to the study of margins (see, e.g., Schapire et al., 1998; Koltchinskii & Pachenko, 2002; Breiman, 1998b; Smola, Bartlett, Schölkopf, & Schuurmans, 2000; Blanchard, 2003; Meir & Rätsch, 2003), the main object of interest in this letter.”
A Survey of Recent Advances in Face Detection (2010, methodology): “For an introduction on boosting, we refer the readers to [59] and [19].”
Activity recognition in beach volleyball using a Deep Convolutional Neural Network (2017, methodology): “1994), boosting (Meir and Rätsch 2003) and bagging (Breiman 1996).”
Diet and dioxins: the need to cut back. (2004, methodology): “We also propose another method in which we use the GPboost program [34] to build boosted classifiers [ 36 ].”
A survey on face detection in the wild: Past, present and future (2015, methodology): “For an introduction on boosting, we refer the readers to [54] and [55].”
Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder. (2007, background): “A very different exposition than ours is the overview of boosting by Meir and Rätsch (author?) [66].”
Activity Recognition from Accelerometer Data (2005, methodology): “• Boosting ( Meir & Ratsch 2003 ) is used to improve the classification accuracy of any given base-level classifier.”
Covariate Shift Adaptation by Importance Weighted Cross Validation (2007, methodology): “, 2007), boosting (Schapire, 2003; Meir and Rätsch, 2003) could be useful bases for further development.”
On the Dual Formulation of Boosting Algorithms (2009, unknown): “✦”
Adaptive boosting for SAR automatic target recognition (2007, methodology): “using the margin theory [10, 11], that is, to relate AdaBoost to those algorithms that directly maximize the classifier margin (or simply margin) defined as”
NARX-Based Nonlinear System Identification Using Orthogonal Least Squares Basis Hunting (2008, methodology): “The RWBS algorithm is a simple yet efficient global search algorithm that adopts some ideas from boosting [36]–[39].”
Large Scale Multiple Kernel Learning (2006, methodology): “It has been shown that solving semi-infinite problems like (7), using a method related to boosting (e.g. Meir and Rätsch, 2003) one requires at most T = O(log(M )/ˆ 2 ) iterations, wherê is the remaining constraint violation and the constants may depend on the kernels and the number of examples N…”