On Nonparametric Discrimination Using Density Differences
提出一种非参数判别技术，通过联合选择平滑参数来最小化两个密度之间的差异，适用于分类、连续和混合数据，并利用两个总体的信息确定平滑参数。
We propose a technique for nonparametric discrimination in which smoothing parameters are chosen jointly, according to a criterion based on the difference between two densities. The approach is suitable for categorical, continuous and mixed data, and uses information from both populations to determine the smoothing parameter for any one population. In the case of categorical data, optimal performance is sometimes achieved using negative smoothing parameters, a property which does not emerge if the smoothing parameters are chosen individually.