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中文摘要：
对基于均衡约束数学规划（MPEC）的数据分类模型进行改进．在确定数据所服从分布的密度函数（高斯混合模型来模拟）的参数时，使用β似然估计来代替原模型中的最大似然估计．新模型可以克服似然函数可能出现无界的现象，在计算上有更好的鲁棒性．对于所得MPEC分类模型，使用filterSQP方法将其作为非线性规划求解．数值试验显示了新模型的有效性．
英文摘要：
In this paper, we provide an improved form of MPEC model for data classification first proposed in Ref. [ 1 ]. We use β likelihood estimation instead of maximum likelihood estimation to estimate the parameters of data＇ s probability distribution function （modeled by Gaussian mixture models）. Our new model can avoid the contingent of unboundedness of the maximum likelihood function and excessive sensitivity of the maximum likelihood estimator to outliers, showing more robustness. Then we use filterSQP method to solve our β likelihood MPEC model as nonlinear program. Efficiency of the model is shown by primal numerical tests.
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