Leonid Karlinsky, Joseph Shtok, et al.
CVPR 2019
We propose a method for constructing regression trees with range and region splitting. We present an efficient algorithm for computing the optimal two-dimensional region that minimizes the mean squared error of an objective numeric attribute in a given database. As two-dimensional regions, we consider a class R of grid-regions, such as "x-monotone," "rectilinear-convex," and "rectangular," in the plane associated with two numeric attributes. We compute the optimal region R ε R. We propose to use a test that splits data into those that lie inside the region R and those that lie outside the region in the construction of regression trees. Experiments confirm that the use of region splitting gives compact and accurate regression trees in many domains.
Leonid Karlinsky, Joseph Shtok, et al.
CVPR 2019
Annina Riedhauser, Viacheslav Snigirev, et al.
CLEO 2023
Gang Liu, Michael Sun, et al.
ICLR 2025
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019