Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

Authors

Terence Sanger

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.