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ML-Progs: More "Research-Level" Implementations of Learning Algorithms

areas/learning/systems/utexas/progs/
This directory contains a copy of more "research-level" versions of the inductive classification algorithms and software for automated experiments that generate learning curves that compare several systems. Programs include: 1. AQ 2. Bayes independent classification system. 3. COBWEB 4. DEDUCE 5. Extentron 6. FOIL (First Order Inductive Learning) in Lisp and Prolog 7. ID3 8. K-Nearest Neighbor Algorithm 9. Perceptron 10. Backprop 11. A dual propositional version of FOIL for learning CNF. 12. A propositional version of FOIL for learning DNF. 13. Functions for running statistical t-test on results of experiments.
Origin:   

   cs.utexas.edu:/pub/mooney/ml-progs/

Requires: Common Lisp, Prolog Copying: Copyright (c) 1991 by Raymond J. Mooney Use permitted for educational and research purposes only. Users are requested, but not required, to inform Raymond J. Mooney of any noteworthy uses of this software. Please see the file readme.txt for details. CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): Raymond J. Mooney, Jude W. Shavlik, Paul Baffes, John Zelle Contact: Raymond J. Mooney Assistant Professor Department of Computer Sciences University of Texas at Austin Keywords: AQ, Authors!Baffes, Authors!Mooney, Authors!Zelle, Backpropagation, Bayes Classification, CNF!FOIL, COBWEB, DEDUCE, DNF!FOIL, Extentron, FOIL, ID3, Inductive Classification, K Nearest Neighbor, Lisp!Code, Machine Learning, Perceptrons, Prolog!Code, Statistics, T-Test, Teaching Materials References: Quinlan, J. R., "Learning Logical Definitions from Relations," in Machine Learning, 5, 1990.
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