Median Predictive Cost of Error with an Asymmetric Cost Function
研究了在误差成本函数不对称时，以预测误差的中位数成本最小化为准则的问题，并与基于均值成本最小化的常规解法进行了比较。对于高斯过程，中位数解是对预测均值的一个简单加法调整，计算远易于期望成本解法。
The problem of reducing predictive cost is considered in the case when the cost of error function is not symmetric and the optimality criterion is the minimization of the median cost of the error of prediction. Examples are given and comparisons made with the usual solution based on the minimization of the mean cost. In the case of a Gaussian process, the median solution is found to be a simple additive adjustment to the predictive mean, and far easier to compute than the solution based on expected cost.