The MONK's Problems A Performance Comparison of Different Learning Algorithms (1991)
| Citations: | 164 - 15 self |
BibTeX
@TECHREPORT{Thrun91themonk's,
author = {S. B. Thrun and J. Bala and E. Bloedorn and I. Bratko and B. Cestnik and J. Cheng and K. De Jong and S. Dzeroski and S. E. Fahlman and D. Fisher and R. Hamann and K. Kaufman and S. Keller and I. Kononenko and J. Kreuziger and R.S. Michalski and T. Mitchell and P. Pachowicz and Y. Reich and H. Vafaie and W. Van De Welde and W. Wenzel and J. Wnek and J. Zhang},
title = {The MONK's Problems A Performance Comparison of Different Learning Algorithms},
institution = {},
year = {1991}
}
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Abstract
This report summarizes a comparison of different learning techniques which was performed 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, AQ17FCLS, AQ14-NT, AQ15-GA, Assistant Professional, mFOIL, ID5R, IDL, ID5R-hat, TDIDT, ID3, AQR, CN2, CLASS WEB, 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 a 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 t...
Citations
| 235 | Faster-learning variations on back-propagation: An empirical study - Fahlman - 1988 |
