ML Anthology

Explicit Representation of Concept Negation

Jean-Francois Puget
MLJ 1994 pp. 233-247
doi:10.1023/A:1022630301359 /mlj/1994/puget1994mlj-explicit/

Abstract

We present a learning method called Negative Explanation Based Generalization (NEBG) that performs automatic changes of representation by computing the negation of an already known concept. NEBG is similar to EBG as a deductive and valid learning method using a single example. It is based on new logic programming techniques based on example-guided transformation of the completed database. We also introduce a very powerful heuristic based on functional properties of the application domain. The implemented algorithms are described and several examples are given.

PDF MLJ Semantic Scholar

Cite

Text

Puget. "Explicit Representation of Concept Negation." Machine Learning, 1994. doi:10.1023/A:1022630301359

Markdown

[Puget. "Explicit Representation of Concept Negation." Machine Learning, 1994.](https://mlanthology.org/mlj/1994/puget1994mlj-explicit/) doi:10.1023/A:1022630301359

BibTeX

@article{puget1994mlj-explicit,
  title     = {{Explicit Representation of Concept Negation}},
  author    = {Puget, Jean-Francois},
  journal   = {Machine Learning},
  year      = {1994},
  pages     = {233-247},
  doi       = {10.1023/A:1022630301359},
  volume    = {14},
  url       = {https://mlanthology.org/mlj/1994/puget1994mlj-explicit/}
}