Nips90 Preprint available from neuroprose archive

[Terence D. Sanger]

The following preprint is available, and will appear in the Nips'90
proceedings:

------------------------------------------------------------------------------

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

			   Terence D. Sanger

  Local variable selection has proven to be a powerful technique for
  approximating 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.

------------------------------------------------------------------------------

Bibtex entry:

  @INCOLLECTION{sanger91,
	AUTHOR = {Terence D. Sanger},
	TITLE = {Basis-Function Trees as a Generalization of Local 
	Variable Selection Methods for Function Approximation},
	BOOKTITLE = {Advances in Neural Information Processing Systems 3},
	PUBLISHER = {Morgan Kaufmann},
	YEAR = {1991},
	EDITOR = {Richard P. Lippmann and John Moody and David S. Touretzky},
	NOTE = {Proc. NIPS'90, Denver CO}
  }

This paper can be obtained by anonymous ftp from the neuroprose database:

unix> ftp cheops.cis.ohio-state.edu          # (or ftp 128.146.8.62)
Name (cheops.cis.ohio-state.edu:): anonymous
Password (cheops.cis.ohio-state.edu:anonymous): <ret>
ftp> cd pub/neuroprose
ftp> binary
ftp> get sanger.trees.ps.Z
ftp> quit
unix> uncompress sanger.trees.ps
unix> lpr -P(your_local_postscript_printer) sanger.trees.ps
		# in some cases you will need to use the -s switch to lpr.


Terry Sanger
MIT, E25-534
Cambridge, MA 02139
USA

tds at ai.mit.edu