L1-regularized quadratic-surface SVMs are convex with unique generic solutions and provably reduce to standard SVMs on linearly separable data, but their advertised sparse-pattern recovery is not rigorously established.
Softquadraticsurfacesupportvector machine for binary classification.Asia-Pacific Journal of Operational Research, 33(06):1650046, 2016
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Quadratic Surface Support Vector Machine with L1 Norm Regularization
L1-regularized quadratic-surface SVMs are convex with unique generic solutions and provably reduce to standard SVMs on linearly separable data, but their advertised sparse-pattern recovery is not rigorously established.