Escaping the Curse of Dimensionality with a Tree-Based Regressor
Abstract
We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.
Cite
Text
Kpotufe. "Escaping the Curse of Dimensionality with a Tree-Based Regressor." Annual Conference on Computational Learning Theory, 2009.Markdown
[Kpotufe. "Escaping the Curse of Dimensionality with a Tree-Based Regressor." Annual Conference on Computational Learning Theory, 2009.](https://mlanthology.org/colt/2009/kpotufe2009colt-escaping/)BibTeX
@inproceedings{kpotufe2009colt-escaping,
title = {{Escaping the Curse of Dimensionality with a Tree-Based Regressor}},
author = {Kpotufe, Samory},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2009},
url = {https://mlanthology.org/colt/2009/kpotufe2009colt-escaping/}
}