ML Anthology

Learning Taxonomic Relation by Case-Based Reasoning

Ken Satoh
ALT 2000 pp. 179-193
doi:10.1007/3-540-40992-0_14 /alt/2000/satoh2000alt-learning/

Abstract

In this paper, we propose a learning method of minimal casebase to represent taxonomic relation in a tree-structured concept hierarchy. We firstly propose case-based taxonomic reasoning and show an upper bound of necessary positive cases and negative cases to represent a relation. Then, we give an learning method of a minimal casebase with sampling and membership queries. We analyze this learning method by sample complexity and query complexity in the framework of PAC learning.

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Cite

Text

Satoh. "Learning Taxonomic Relation by Case-Based Reasoning." International Conference on Algorithmic Learning Theory, 2000. doi:10.1007/3-540-40992-0_14

Markdown

[Satoh. "Learning Taxonomic Relation by Case-Based Reasoning." International Conference on Algorithmic Learning Theory, 2000.](https://mlanthology.org/alt/2000/satoh2000alt-learning/) doi:10.1007/3-540-40992-0_14

BibTeX

@inproceedings{satoh2000alt-learning,
  title     = {{Learning Taxonomic Relation by Case-Based Reasoning}},
  author    = {Satoh, Ken},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {2000},
  pages     = {179-193},
  doi       = {10.1007/3-540-40992-0_14},
  url       = {https://mlanthology.org/alt/2000/satoh2000alt-learning/}
}