🚀 go-pugleaf

The Machine-Learning-Program-Library with PROLOG implementations of
basic Machine Learning algorithms has been moved from the ftp-server
of the University of Osnabrueck to the ftp-server of the German 
National Research Center for Computer Science (GMD).

The programs are  now accessible via ftp from 'ftp.gmd.DE' within the
directory 'gmd/mlt/ML-Program-Library'.

Please, consult the attached README-file of the library for further
details.

% file: gmd/mlt/ML-Program-Library/README:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%            This is the Machine Learning Program Library
%                              of the
%        Special Interest Group on Machine Learning (FG 1.1.3)
%        of the German Society for Computer Science (GI e.V.)
%                         7 January 1993
% Anonymous ftp-Server: ftp.gmd.DE (129.26.8.90)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Included  in  this  library  are   several  PROLOG implementations  of
basic machine learning algorithms.  The contents of the repository can
remotely copied    to    other    network    sites    via    ftp  from
'ftp.gmd.de'.  The login-name  is 'anonymous', as  password enter your 
own e-mail address. To find the program directories  with the programs, 
some small test  data  sets  and  demonstration LOG files enter
      cd gmd/mlt/ML-Program-Library

The names and addresses of  the authors,  references to  the origin of
the algorithms, and hints how the programs can be  started are usually
included in the program files.

The file LOGFILE lists changes and modifications to the library.  This
file should make it  easy for  you to  determine what's  new since you
last looked  at  it.   

Notes  
----- 

1) Software delivery: If you have implemented a  basic machine learning
   algorithm in PROLOG, which is free of copyrights, please  send it to  
   Thomas Hoppe. Order his software documentation file for more details.

2) Bug detection: The algorithms  are more  or less  tested.  Somtimes
   bugs may  occur  as  a  consequence of  the subtil  differences  of 
   the  different PROLOG  dialects  (especially  built-in predicates).
   If  you  find  a  'new feature',  which  you did not expect, inform 
   Thomas Hoppe so that others can take benefit from your experience.

3) Copyright: Please  note  the remarks  on the  copyright and allowed
   modifications made  by some  program authors  at the  beginning  of 
   the program files.

4) The  files  may  also  be ordered  via surface  or electronic mail.
   People without access  to the  archive should  send a  short notice 
   to Thomas Hoppe using the address given below.

5) We appreciate to draw your attention to the fact that the Knowledge
   Acquisition and Machine Learning System MOBAL (2.0) is also accesible
   accessible  via  the same anonymous  ftp server. The  system, a user
   guide and a README file   are located in the  directory
          gmd/mlt/Mobal 
   (MOBAL has been developed using QUINTUS PROLOG 3.1.1 on a SUN4).
   
 
Brief Overview of the Program Library
-------------------------------------

Each sub-directory contains a PROLOG (re-)implementation of a basic
machine learning algorithm, one (or more) test data files, and (in
some cases) a small log file produced by running the program on
the test data set using QUINTUS PROLOG (release 2.4). 
  
  README           this file
  LOGFILE          description of last changes and additions
  aq1/
    aq1.pro        reimplementation of Jeffrey M. Becker's AQ-PROLOG (based
                   on Michalski's AQ)  (author: Thomas Hoppe)
    aq1_1.pro      a simple data set
    aq1_2.pro      Extensions to aq1_1.pro		    
  arch1/
    arch1.pro      Winston's incremental learning procedure for
                   structural descriptions (author: Stefan Wrobel)
    arch1_1.pro    Winston's example archs
    arch1.log	   Log-file of a sample run
  arch2/
    arch2.pro      a minimal implementation of Winstons's ARCH
                   (author: Ivan Bratko)
    arch2_1.pro    a small test set  		   
    arch2.log      Log-file of a sample run
  attdsc/
    attdsc.pro     Ivan Bratko's algorithm for learning attributional
                   descriptions
    attdsc_1.pro   Small example set for learning to recognize objects
                   from their silhouettes
  cobweb/
    cobweb.pro     a PROLOG implementation of Fisher's COBWEB using
                   CLASSIT's evaluation function to deal with numeric
		   attributes (author: Joerg-Uwe Kietz)
    cobweb_1.pro   a simple data set describing some hotels (numeric and
                   nominal attributes)
    cobweb_2.pro   Gennari, Langley, and Fisher's rectangle 
                   classification example (numeric attributes)
    cobweb_3.pro   Fisher's animal classification example (nominal 
                   attributes)
    cobweb_4.pro   Gennari, Langley, and Fisher's cell classification 
                   example (numeric attributes)
    cobweb.log     Log-file of running the program the example data 
                   sets   
  discr/
    discr.pro      Brazdil's generation of discriminations from
                   derivation trees (author: Thomas Hoppe)
    discr_1.pro    Simple abstract example
    discr_2.pro    Abstract example generating useful and not
  ebg/
    ebg.pro        Basic algorithms for explanation based generalisa-
                   tion and partial evaluation based on Kedar-Cabelli
                   & McCarty's idea. Different kinds of simple PROLOG
                   meta-interpreters.
    ebg_1.pro      Suicide example for EBG
    ebg_2.pro      Safe_to_stack example for EBG
                   useful descriminants  (author: Thomas Hoppe)
  id3/
    idt.pro       ID3.1 Implementation of Quinlan's ID3 algorithm
                   based on the 'gain-ratio'-measure
		   (authors: Luis Torgo, Thomas Hoppe)
    idt_1.pro     simple example data set
    idt_2.pro     simple example data set
    idt_3.pro     simple example data set
  invers/
    invers.pro     Implementation of absorption and intra-construction
                   operators for inverse resolution
		   (author: Thomas Hoppe)
    invers_1.pro   example calls		   		   
  logic/
    logic.pro      Substitution matching, term generalizations,
                   generalized subsumption
    logic_1.pro    Example calls        
  multagent/
    multagent.pro  Yiu Cheung HO's implementation of Brazdil's tutoring 
                   setting
    teacher.pro    Teacher's knowledge base
    learner1.pro   A correct Learner's knowledge base
    learner2.pro   An erroneous Learner's knowledge base
    calls_1.pro    Example calls concerning correct knowledge
    calls_2.pro    Example calls concerning wrong knowledge
  vs/
    vs.pro         Implementation of Mitchell's version space algorithm  
    vs_1.pro       a simple shape and color taxonomy
    vs_1.log       Log-file of a sample run
 
 
 
 
Suggestions and complaints regarding the access to the ftp-library 
or the Log-files are welcome any time by Werner Emde.

Additional PROLOG implementations  of Machine  Learning Algorithms are
welcome by Thomas Hoppe who is responsible for the  maintenance of the
program library.  Thomas Hoppe has made slight changes to the programs
supplied by the different authors in order to make them independent of
a specific PROLOG dialect (as far as possible).


  Thomas Hoppe                         Dr. Werner Emde
  Projektgruppe KIT                    GMD, FIT.KI
  Technische Universitaet Berlin       Postfach 13 16
  Franklinstr. 28/29                   Schloss Birlinghoven 
  D-1000 Berlin 10                     D-W-5205 Sankt Augustin 1
  Germany                              Germany
                
  email: hoppet@cs.tu-berlin.de        email:  werner.emde@gmd.de
  Phone: +49.30.314-25494              Phone:  +49.2241.14-2282       
  FAX:   +49.30.314-24929              FAX:    +49.2241.14-2072