ML Anthology

Case-Based Learning: Beyond Classification of Feature Vectors

David W. Aha, Dietrich Wettschereck
ECML-PKDD 1997 pp. 329-336
doi:10.1007/3-540-62858-4_96 /ecmlpkdd/1997/aha1997ecml-casebased/

Abstract

The dominant theme of case-based research at recent ML conferences has been on classifying cases represented by feature vectors. However, other useful tasks can be targeted, and other representations are often preferable. We review the recent literature on case-based learning, focusing on alternative performance tasks and more expressive case representations. We also highlight topics in need of additional research.

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Cite

Text

Aha and Wettschereck. "Case-Based Learning: Beyond Classification of Feature Vectors." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_96

Markdown

[Aha and Wettschereck. "Case-Based Learning: Beyond Classification of Feature Vectors." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/aha1997ecml-casebased/) doi:10.1007/3-540-62858-4_96

BibTeX

@inproceedings{aha1997ecml-casebased,
  title     = {{Case-Based Learning: Beyond Classification of Feature Vectors}},
  author    = {Aha, David W. and Wettschereck, Dietrich},
  booktitle = {European Conference on Machine Learning},
  year      = {1997},
  pages     = {329-336},
  doi       = {10.1007/3-540-62858-4_96},
  url       = {https://mlanthology.org/ecmlpkdd/1997/aha1997ecml-casebased/}
}