Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell
TL;DR通过建立 full density 模型 f(yjx) 而非只有期望值 E(yjx),条件密度估计广义了回归。本文提出了双核条件密度估计器,并引入了基于双数树的快速算法,用最大似然准则进行带宽选择,从而在处理多变量数据集时取得 380 万倍的加速。
Abstract
conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx). This is important for many tasks, including handling multi-modality and generating prediction intervals. Though fundamental and widely applicable, nonpara