Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation
Abstract
Local variable selection has proven to be a powerful technique for ap(cid:173) proximating functions in high-dimensional spaces. It is used in several statistical methods, including CART, ID3, C4, MARS, and others (see the bibliography for references to these algorithms). In this paper I present a tree-structured network which is a generalization of these techniques. The network provides a framework for understanding the behavior of such algorithms and for modifying them to suit particular applications.
Cite
Text
Sanger. "Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation." Neural Information Processing Systems, 1990.Markdown
[Sanger. "Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/sanger1990neurips-basisfunction/)BibTeX
@inproceedings{sanger1990neurips-basisfunction,
title = {{Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation}},
author = {Sanger, Terence D.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {700-706},
url = {https://mlanthology.org/neurips/1990/sanger1990neurips-basisfunction/}
}