This is version 2.5 of Statistics::LTU, a module for Linear Threshold Units. A linear threshold unit is a 1-layer neural network, also called a perceptron. LTU's are used to learn classifications from examples. An LTU learns to distinguish between two classes based on the data given to it. After training on a number of examples, the LTU can then be used to classify new (unseen) examples. LTU.pm defines an (uninstantiable) base class, LTU, and four other instantiable classes built on top of LTU. Each of the four classes uses a different training method: ACR (absolute correction rule), TACR (a thermal annealing version of the absolute correction rule), LMS (least-mean squares fit) and RLS (recursive least-mean squares rule). Check out ltu.doc for further information on these. You can use LTUs without understanding exactly how they work. REQUIREMENTS Statistics::LTU needs Perl version 5, as it is object-oriented and uses references extensively. INSTALLATION Run the following: perl Makefile.PL make make install LTU.pm has some tests at the end; try running "perl LTU.pm". Note that this creates four LTU files with ".saved" extensions, which can be deleted after the tests. Note: Depending on the version of MakeMakefile you have, you may get an error with ld when you run make. I don't know how to prevent this. The Makefile is just used to copy LTU.pm, LTU.pod and weather.pl into the Statistics subdirectory of your Perl library directory. Then you probably want to: make clean FILES ltu.doc has some useful information on LTUs, though it was written for the C version. weather.perl is a simple demo showing how examples are created, and how LTUs are trained and tested. It should be more instructive than LTU.man. Run "perl weather.perl | more". The code itself is heavily documented. AUTHOR / SUGGESTIONS / BUG MAGNET Tom Fawcett (fawcett@nynexst.com). LTU.pm is based on LTU.C, an implementation of LTUs written by James Callan at the University of Massachusetts. I've tried to Perlify and objectify the code completely, but some awkwardness remains.