Large-Scale Bayesian Logistic Regression for Text Categorization

By A. Genkin, David D. Lewis, David Madigan. Technometrics, 2007

911 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6a10eaedec9d402fb8d8b8900994d0aa27c5e129

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Citations in the corpus

Regression shrinkage and selection via the lasso: a retrospective (2011, neutral): “The same algorithm is contained in the homotopy approach of Osborne et al. (2000). Coordinate descent algorithms are extremely simple and fast, and exploit the assumed sparsity of the model to great advantage.”
Decision Trees for Functional Variables (2006, methodology): “LR (an L 1 constrained logistic regression classifier), using BBR 2 (Genkin et al., 2004) (for binary classification problems) and BMR 3 (for multi-class problems) trained with 10-fold CV used to pick the hyperparameter from amongst the set {0.001, 0.01, 0.1, 1, 10, 100, 1000} .”
Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery (2011, methodology): “Shrinkage regression is an alternative approach that has been successfully applied to the analysis of individual case safety reports in the presence of confounding by co-reported medicines.(22,23) Its main advantage is that it can incorporate a large number of suspected confounders simultaneously an”
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression (2007, methodology): “…generalized iterative scaling (Goodman, 2004), bound optimization algorithms (Krishnapuram et al., 2005), online algorithms (Balakrishnan and Madigan, 2006; Perkins and Theiler, 2003), coordinate descent methods (Friedman et al., 2007; Genkin et al., 2006), and the Gauss-Seidel method…”
Text Classification Algorithms: A Survey (2019, neutral): “LR predicts probabilities rather than classes [118,119].”
Partial Correlation Estimation by Joint Sparse Regression Models (2008, methodology): “s such as lasso (Tibshirani 1996). This algorithm is computationally more eﬃcient than the original shooting algorithm, which was ﬁrst proposed by Fu (1998) and then extended by many others including Genkin et al. (2007) and Friedman et al. (2007a). It enables us to implement the proposed procedure ”
L1‐regularization path algorithm for generalized linear models (2007, methodology): “As applied to regression (Tibshirani, 1996, 1997) and classification methods (Genkin et al., 2004; Shevade and Keerthi, 2003; Zhu et al., 2003), penalizing the size of the L1-norm of the coefficients is useful because it accompanies variable selection.”
Least angle and ℓ1 penalized regression: A review (2008, methodology): “ensional settings, by making use of the preceding solution as starting values. Coordinate-wise optimization techniques have shown their success with other penalty types and also for nonlinear models (Genkin et al., 2007; Yuan and Lin, 2006; Meier et al., 2008; Friedman et al., 2007). Osborne et al. ”
Recent Advances of Large-Scale Linear Classification (2012, methodology): “, an interior-point method (l1 logreg [33]), (block) coordinate descent methods (BBR [34] and CGD [35]), a quasi-Newton method (OWL-QN [36]), Newton-type methods (GLMNET [37] and LIBLINEAR [19]), and a Nesterov’s method (SLEP [38]).”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “in terms of uncertainty quantification, there is scope for using Bayesian regression analysis (Fernandez and Steel, 2000) with Bayesian linear and logistic regression (Genkin et al., 2007) in the scope of remote sensing (Ruiz et al.”
Computational methods in authorship attribution (2009, neutral): “…vector machines (De Vel et al. 2001; Diederich et al. 2003; Koppel & Schler 2003, Abbasi & Chen 2005; Koppel et al. 2005; Zheng et al 2006), Winnow (Koppel et al. 2002; Argamon et al. 2003; Koppel et al. 2006a), and Bayesian regression (Genkin et al. 2006; Madigan et al. 2006; Argamon et al. 2008).”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, background): “Neural networks [42] and Bayesian logistic regression [43]”
Penalized regression, standard errors, and Bayesian lassos (2010, methodology): “Genkin et al. (2007) presented a simple Bayesian logistic lasso with Laplace prior to avoid overfitting and produce sparse predictive models for text data.”
Advancing the Science for Active Surveillance: Rationale and Design for the Observational Medical Outcomes Partnership (2010, methodology): “Other methods Bayesian logistic regression (32, 33) Columbia University This is a high-dimensional statistical method that is scalable to a substantial number of covariates, accommodating all drugs and conditions in a single model to predict occurrence of ADEs.”
Distributed Coordinate Descent for L1-regularized Logistic Regression (2014, methodology): “Widely used algorithms that fall into this family are: BBR [6], GLMNET [5], newGLMNET [16].”