%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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.) % 8 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 %--- end-of-file