Machine Learning via Polyhedral Concave Minimization

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Block-Coordinate Frank-Wolfe Optimization for Structural SVMs (2012, methodology): “To solve the dual structural SVM problem, in this paper we consider the Frank-Wolfe (1956) algorithm, which has seen a recent surge of interest in machine learning and signal processing (Mangasarian, 1995; Clarkson, 2010; Jaggi, 2011; 2013; Bach et al., 2012), including in the context of binary SVMs”
Discrete support vector decision trees via tabu search (2004, background): “From one side, some papers (Mangasarian, 1994, 1996; Chunhui Chen and Mangasarian, 1996; La Torre and Vercellis, 2003) focused on the misclassi0cation rate alone, not in conjunction with the margin minimization, and transformed the discrete problem into a nonlinear optimization model by means of…”
Feature Selection via Concave Minimization and Support Vector Machines (1998, methodology): “In the concave minimization approach [19, 5] a separating plane is generated by minimizing a weighted sum of distances of misclassi ed points to two parallel planes that bound the sets and which determine the separating plane midway between them.”