Machine Learning via Polyhedral Concave MinimizationO

@inproceedings{Mangasarian1996MachineLV,
  title={Machine Learning via Polyhedral Concave MinimizationO},
  author={L. Mangasarian},
  year={1996}
}
  • L. Mangasarian
  • Published 1996
Two fundamental problems of machine learning, misclassiication minimization 10, 24, 18] and feature selection, 25, 29, 14] are formulated as the minimization of a concave function on a 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… CONTINUE READING

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