Knowledge-based semidefinite linear programming classifiers
In this paper, we present knowledge-based support vector machine (SVM) classifiers using semidefinite linear programming. SVMs are an optimization-based solution method for large-scale data classification problems. Knowledge-based SVM classifiers, where prior knowledge is in the form of ellipsoidal constraints, result in a semidefinite linear programme with a set containment constraint. These problems are reformulated as standard semidefinite linear programming problems by the application of a dual characterization of the set containment under a mild regularity condition. The reformulated semidefinite linear programme is solved by the publicly available solvers. Computational results show that prior knowledge can often improve correctness of the classifier.
support vector machines set containment characterizations ellipsoidal knowledge sets semidefinite programming