ML Anthology

The Utility of Knowledge in Inductive Learning

Michael J. Pazzani, Dennis F. Kibler
MLJ 1992 pp. 57-94
doi:10.1007/BF00993254 /mlj/1992/pazzani1992mlj-utility/

Abstract

In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating function-free Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these forms of knowledge have on the cost and accuracy of learning. Lastly, we demonstrate that a hybrid explanation-based and inductive learning method can advantageously use an approximate domain theory, even when this theory is incorrect and incomplete.

PDF MLJ Semantic Scholar

Cite

Text

Pazzani and Kibler. "The Utility of Knowledge in Inductive Learning." Machine Learning, 1992. doi:10.1007/BF00993254

Markdown

[Pazzani and Kibler. "The Utility of Knowledge in Inductive Learning." Machine Learning, 1992.](https://mlanthology.org/mlj/1992/pazzani1992mlj-utility/) doi:10.1007/BF00993254

BibTeX

@article{pazzani1992mlj-utility,
  title     = {{The Utility of Knowledge in Inductive Learning}},
  author    = {Pazzani, Michael J. and Kibler, Dennis F.},
  journal   = {Machine Learning},
  year      = {1992},
  pages     = {57-94},
  doi       = {10.1007/BF00993254},
  volume    = {9},
  url       = {https://mlanthology.org/mlj/1992/pazzani1992mlj-utility/}
}