The MONK's Problems-A Performance Comparison of Different Learning Algorithms

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