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dc.contributor.authorMangasarian, O. L.
dc.description.abstractTwo fundamental problems of machine learning, misclassification minimization [10,24,18] and feature selection, [25, 29, 14] are formulated as the minimization of a concave function on the polyhedral set. Other formulations of these problems utilize linear programs with equilibrium constraints [18, 1, 4, 3] which are generally intractable. In contrast, for the proposed concave minimization formulation, a successive linearization algorithm without stepsize terminates after a maximum average of 7 linear programs on problems with as many as 4192 points in 14 dimensional space the algorithm terminates at a stationary point or a global solution to the problem. Preliminary numerical results indicate that the proposed approach is quite effective and more efficient than other approaches.en
dc.titleMachine Learning via Polyhedral Concave Minimizationen

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  • Math Prog Technical Reports
    Math Prog Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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