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The classi cation problem of constructing a plane to separate the members of two sets can be formulated as a parametric bilinear program. This approach was originally created to minimize the number of points misclassi ed. However, a novel interpretation of the algorithm is that the subproblems represent alternative error functions of the misclassi ed points. Each subproblem identi es a speci ed number of outliers and minimizes the magnitude of the errors on the remaining points. A tuning set is used to select the best result amoung the subproblems. A parametric Frank-Wolfe method was used to solve the bilinear subproblems. Computational results on a number of datasets indicate that the results compare very favorably with linear programming and simulated annealing approaches. The algorithm can be used as part of a decision tree algorithm to create nonlinear classi ers.

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