ML Anthology

Learning Prototypical Concept Descriptions

Piew Datta, Dennis F. Kibler
ICML 1995 pp. 158-166
doi:10.1016/B978-1-55860-377-6.50028-1 /icml/1995/datta1995icml-learning/

Abstract

We describe a new representation for learning concepts that differs from the traditional decision tree and rule approach. This representation, called prototypical concept descriptions, can represent several prototypes for a concept. We also describe PL, our algorithm for learning these prototypes, and demonstrate that prototypical concept descriptions can, in some situations, classify more accurately than standard Machine Learning algorithms. More importantly, we show that they yield more stable descriptions when applied in noisy and dynamic situations.

ICML Semantic Scholar

Cite

Text

Datta and Kibler. "Learning Prototypical Concept Descriptions." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50028-1

Markdown

[Datta and Kibler. "Learning Prototypical Concept Descriptions." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/datta1995icml-learning/) doi:10.1016/B978-1-55860-377-6.50028-1

BibTeX

@inproceedings{datta1995icml-learning,
  title     = {{Learning Prototypical Concept Descriptions}},
  author    = {Datta, Piew and Kibler, Dennis F.},
  booktitle = {International Conference on Machine Learning},
  year      = {1995},
  pages     = {158-166},
  doi       = {10.1016/B978-1-55860-377-6.50028-1},
  url       = {https://mlanthology.org/icml/1995/datta1995icml-learning/}
}