ML Anthology

A Knowledge-Intensive Approach to Learning Relational Concepts

Michael J. Pazzani, Clifford Brunk, Glenn Silverstein
ICML 1991 pp. 432-436
doi:10.1016/B978-1-55860-200-7.50089-1 /icml/1991/pazzani1991icml-knowledge/

Abstract

We describe a new approach to integrating explanation-based and empirical learning methods for learning relational concepts. The approach uses an information-based heuristic to evaluate components of a hypothesis that are proposed either by explanation-based or empirical learning methods. Providing domain knowledge to the integrated system can decrease the amount of search required during learning and increase the accuracy of learned concepts, even when the domain knowledge is incorrect and incomplete.

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Cite

Text

Pazzani et al. "A Knowledge-Intensive Approach to Learning Relational Concepts." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50089-1

Markdown

[Pazzani et al. "A Knowledge-Intensive Approach to Learning Relational Concepts." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/pazzani1991icml-knowledge/) doi:10.1016/B978-1-55860-200-7.50089-1

BibTeX

@inproceedings{pazzani1991icml-knowledge,
  title     = {{A Knowledge-Intensive Approach to Learning Relational Concepts}},
  author    = {Pazzani, Michael J. and Brunk, Clifford and Silverstein, Glenn},
  booktitle = {International Conference on Machine Learning},
  year      = {1991},
  pages     = {432-436},
  doi       = {10.1016/B978-1-55860-200-7.50089-1},
  url       = {https://mlanthology.org/icml/1991/pazzani1991icml-knowledge/}
}