论文标题
对数线性时间顺序最佳校准算法，用于量化的等距L2回归
A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regression
论文作者
论文摘要
我们研究了量化的等音L2回归设置中估计的顺序校准。首先，我们表明可以从传统的等渗L2回归解决方案中获取最佳校准量化估计。我们修改了传统的PAVA算法，以创建校准器，用于量化和顺序优化量化的等渗回归问题。我们的算法可以更新到迄今为止在线性空间和每个新的无序样本中观察到的样品的最佳量化单调映射。
We study the sequential calibration of estimations in a quantized isotonic L2 regression setting. We start by showing that the optimal calibrated quantized estimations can be acquired from the traditional isotonic L2 regression solution. We modify the traditional PAVA algorithm to create calibrators for both batch and sequential optimization of the quantized isotonic regression problem. Our algorithm can update the optimal quantized monotone mapping for the samples observed so far in linear space and logarithmic time per new unordered sample.
