Encouraging Experimental Results on Learning CNF

Mooney, Raymond J.

doi:10.1007/BF00994661

Encouraging Experimental Results on Learning CNF

Raymond J. Mooney

MLJ 1995 pp. 79-92

doi:10.1007/BF00994661 /mlj/1995/mooney1995mlj-encouraging/

Abstract

This paper presents results comparing three simple inductive learning systems using different representations for concepts, namely: CNF formulae, DNF formulae, and decision trees. The CNF learner performs surprisingly well. Results on five natural data sets indicates that it frequently trains faster and produces more accurate and simpler concepts.

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Text

Mooney. "Encouraging Experimental Results on Learning CNF." Machine Learning, 1995. doi:10.1007/BF00994661

Markdown

[Mooney. "Encouraging Experimental Results on Learning CNF." Machine Learning, 1995.](https://mlanthology.org/mlj/1995/mooney1995mlj-encouraging/) doi:10.1007/BF00994661

BibTeX

@article{mooney1995mlj-encouraging,
  title     = {{Encouraging Experimental Results on Learning CNF}},
  author    = {Mooney, Raymond J.},
  journal   = {Machine Learning},
  year      = {1995},
  pages     = {79-92},
  doi       = {10.1007/BF00994661},
  volume    = {19},
  url       = {https://mlanthology.org/mlj/1995/mooney1995mlj-encouraging/}
}