LMS-tree source code available

[Terence D. Sanger]

Source code for a sample implementation of the LMS-tree algorithm is now
available by anonymous ftp.  The code includes a bare-bones implementation
of the algorithm incorporated into a demo program which predicts future
values of the mackey-glass differential delay equation.  The demo will run
under X11R3 or higher, and has been tested on sun-3 and sun-4 machines.
Since this is a deliberately simple implementation not including tree
pruning or other optimizations, many improvements are possible.  I
encourage any and all suggestions, comments, or questions.

				Terry Sanger  (tds at ai.mit.edu)

To obtain and execute the code:

> mkdir lms-trees
> cd lms-trees
> ftp ftp.ai.mit.edu
login: anonymous
password: <ret>
ftp> cd pub
ftp> binary
ftp> get sanger.mackey.tar.Z
ftp> quit
> uncompress sanger.mackey.tar.Z
> tar xvf sanger.mackey.tar
> make mackey
> mackey


Some documentation is included in the header of the file mackey.c .  A
description of the algorithm can be found in the paper I recently announced
on this network:

  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
approximating functions in high-dimensional spaces.  It is used in several
statistical methods, including CART, ID3, C4, MARS, and others .  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. 


To obtain the paper:

>ftp cheops.cis.ohio-state.edu
login: anonymous
password: <ret>
ftp> cd pub/neuroprose
ftp> binary
ftp> get sanger.trees.ps.Z
ftp> quit
>uncompress sanger.trees.ps.Z
>lpr -h -s sanger.trees.ps

Good Luck!