A Comparison of Linear Programming and Parametric Approaches to the Two‐Group Discriminant Problem*
比较了15种线性规划判别模型与Fisher线性判别函数、Smith二次判别函数在正态分布数据上的分类准确率，发现Smith二次判别函数最准确，建议进一步测试非高斯数据。
Recent simulation‐based studies of linear programming models for discriminant analysis have used the Fisher linear discriminant function as the benchmark for parametric methods. This article reports experimental evidence which suggests that, while some linear programming models may match or even exceed the Fisher approach in classification accuracy, none of the fifteen models tested is as accurate on normally distributed data as the Smith quadratic discriminant function. At the minimum, further testing is warranted with an emphasis on data sets that arise from significantly non‐Gaussian populations.