Multivariate classification trees based on minimum features discrete support vector machines

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Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, methodology): “…popular approach has been to extract easy to understand structures from powerful yet black box type classi ers, such as if then rules, decision trees and decision tables (Baesens et al. 2003, Bertsimas et al. 2011, Martens et al. 2007, Martens and Provost 2013, Orsenigo and Vercellis 2003, 2004).”
Optimal randomized classification trees (2021, neutral): “A similar proposal can be found in [40], where the baseline classification procedure is a Support Vector Machine instead.”
Support Vector Machines with the Ramp Loss and the Hard Margin Loss (2011, background): “In practice, the dual form of traditional SVM is solved in part because of the ability to accommodate kernel functions.”
Supervised classification and mathematical optimization (2013, methodology): “One of the most cited feature selection approaches is the recursive feature elimination algorithm for SVMs proposed in [110], a greedy method where the SVM classifier is built and the feature with the smallest absolute value of the coefficient in the separating hyperplane is discarded.”