To appear in Optimization Methods and Software ROBUST LINEAR PROGRAMMING DISCRIMINATION OF TWO LINEARLY INSEPARABLE SETS

A single linear programming formulation is proposed which generates a plane that minimizes an average sum of misclassi ed points belonging to two disjoint points sets in n-dimensional real space. When the convex hulls of the two sets are also disjoint, the plane completely separates the two sets. When the convex hulls intersect, our linear program, unlike all previously proposed linear programs, is guaranteed to generate some error-minimizing plane, without the imposition of extraneous normalization constraints that inevitably fail to handle certain cases. The e ectiveness of the proposed linear program has been demonstrated by successfully testing it on a number of databases. In addition, it has been used in conjunctionwith the multisurfacemethod of piecewiselinear separation to train a feed-forward neural network with a single hidden layer.