Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification

By Mark A. Davenport, Richard Baraniuk, Clayton D. Scott. 2008

78 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “Such formulations can capture uncertainty in the class priors and uncertainty resulting from label noise in classification problems [34], [35].”
Satisfying Real-world Goals with Dataset Constraints (2016, methodology): “to that of Davenport et al. [2010], which differs in that it uses a hinge loss approximation instead of the ramp loss, and found with the ramp-loss they achieved similar or slightly better results with up to 10× less computation. Narasimhan et al. [2015] considered optimizing for the F-measure and o”
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals (2018, methodology): “classification (Davenport et al., 2010; Gasso et al., 2011), and AUC optimization (Eban et al.”
Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “The main robustness considered in classification is against uncertainty in the class frequencies or relative costs of different types of errors [37, 17], but robustness against other uncertainties are also considered [49, 57].”
A Machine Learning Approach to Android Malware Detection (2012, methodology): “This could be combined with [9], in which the authors describe a method for training Support Vector Machines for Neyman-Pearson Classiﬁcation.”
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints (2018, methodology): “Appendix A contains two examples of how Equation 1 can be used to express certain data-dependent constraints (see Goh et al. [2016] and Narasimhan [2018] for more).”
Two-Player Games for Efficient Non-Convex Constrained Optimization (2018, methodology): “for Neyman-Pearson classification (Davenport et al., 2010; Gasso et al., 2011), more general rate metrics (Goh et al.”