Machine Learning via Polyhedral Concave Minimization
نویسنده
چکیده
Two fundamental problems of machine learning misclassi cation minimization and feature selection are formulated as the minimization of a concave function on a polyhedral set Other formulations of these problems utilize linear programs with equilibrium constraints which are generally intractable In contrast for the proposed concave minimization formulation a successive linearization algorithm without stepsize terminates after a maximum average of linear programs on problems with as many as points in 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 e ective and more e cient than other approaches
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