著者
S.B. Thrun , J. Bala, E. Bloedom, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, D. Fisher, S.E. Fahlman , R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S. Michalski, T. Mitchell, P. Pachowics, Y. Reich, H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, J. Zhang
タイトル
The MONK's Problems-A Performance Comparison of Different Learning Algorithms
日時
December 1991
概要
This report summarizes a comparison of different learning techniques which was perfoemed at the 2nd European Summer School on Machine Learning, held in Belgium during summer 1991. A variety of symbolic and non-symbolic learning techniques-namely AQ17-DCI, AQ17-HCI, AQ17-FCLS, AQ14-NT, AQ15-GA, Assistant Professional, mFOIL, ID5R, IDL, ID5R-hat, TDIDT, ID3, AQR, CN2, CLASSWEB, ECOBWEB, PRISM, Backpropagation, and Cascade Correlation - are compared on three classification problems, the MONK'S problems. The MONK's problems are derived from a domain in which each training example is represented by six discrete-valued attributes. Each problem involves learning binary function defined over this domain, from a sample of training examples of this function. Experiments were performed with and without noise in the training examples. One significant characteristic of this comparison is that it was performed by a collection of researchers, each of whom was an advocate of the technique they tested (often they were the creators of the various methods). In this sense, the results are less biased than in comparisons performed by a single person advocating a specific learning method, and more accurastely reflecr the generalization behavior of the learning techniques as applied by knoqledgeable users.
カテゴリ
CMUTR
Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: This report summarizes a comparison of different learning 
        techniques which was perfoemed at the 2nd European Summer School
        on Machine Learning, held in Belgium during summer 1991. A 
        variety of symbolic and non-symbolic learning techniques-namely 
        AQ17-DCI, AQ17-HCI, AQ17-FCLS, AQ14-NT, AQ15-GA, Assistant 
        Professional, mFOIL, ID5R, IDL, ID5R-hat, TDIDT, ID3, AQR, CN2,
        CLASSWEB, ECOBWEB, PRISM, Backpropagation, and Cascade 
        Correlation - are compared on three classification problems, the
        MONK'S problems.
        
        The MONK's problems are derived from a domain in which each 
        training example is represented by six discrete-valued 
        attributes. Each problem involves learning binary function 
        defined over this domain, from a sample of training examples of 
        this function. Experiments were performed with and without noise
        in the training examples.
        
        One significant characteristic of this comparison is that it was
        performed by a collection of researchers, each of whom was an 
        advocate of the technique they tested (often they were the 
        creators of the various methods). In this sense, the results are
        less biased than in comparisons performed by a single person 
        advocating a specific learning method, and more accurastely 
        reflecr the generalization behavior of the learning techniques 
        as applied by knoqledgeable users.
        
        
Number: CMU-CS-91-197
Bibtype: TechReport
Month: dec
Author: S.B. Thrun 
        J. Bala
        E. Bloedom
        I. Bratko
        B. Cestnik
        J. Cheng
        K. De Jong
        S. Dzeroski
        D. Fisher
        S.E. Fahlman 
        R. Hamann
        K. Kaufman
        S. Keller
        I. Kononenko
        J. Kreuziger
        R.S. Michalski
        T. Mitchell
        P. Pachowics
        Y. Reich
        H. Vafaie
        W. Van de Welde
        W. Wenzel
        J. Wnek
        J. Zhang
Title: The MONK's Problems-A Performance Comparison of Different 
        Learning Algorithms
Year: 1991
Address: Pittsburgh, PA
Super: @CMUTR