This paper focuses on optimal unimodal transformation of the score outputs of a univariate learning model under linear loss functions. We demonstrate that the optimal mapping between score values and the target region is a rectangular function. To produce this optimal rectangular fit for the observed samples, we propose a sequential approach that can its estimation with each incoming new sample. Our approach has logarithmic time complexity per iteration and is optimally efficient.

翻译：本文聚焦于在线性损失函数下对单变量学习模型的分数输出进行最优单峰变换。我们证明，分数值与目标区域之间的最优映射是矩形函数。为对观测样本实现这一最优矩形拟合，我们提出了一种顺序方法，该方法能够随着每个新样本的输入迭代更新其估计。我们的方法每次迭代具有对数时间复杂度，且计算效率达到最优。