Machine Learning via Polyhedral Concave MinimizationO

  title={Machine Learning via Polyhedral Concave MinimizationO},
  author={L. Mangasarian},
  • 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

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
7 Extracted Citations
28 Extracted References
Similar Papers

Referenced Papers

Publications referenced by this paper.

Similar Papers

Loading similar papers…