Induction of Feature Terms with INDIE
Abstract
The aim of relational learning is to develop methods for the induction of descriptions in representation formalisms that are more expressive than attribute-value representation. Feature terms have been studied to formalize object-centered representation in declarative languages and can be seen as a subset of first-order logic. We present a representation formalism based on feature terms and we show how induction can be performed in a natural way using a notion of subsumption based on an informational ordering. Moreover feature terms also allow to specify incomplete information in a natural way. An example of such inductive methods, indie , is presented. indie performs bottom-up heuristic search on the subsumption lattice of the feature term space. Results of this method on several domains are explained.
Cite
Text
Armengol and Plaza. "Induction of Feature Terms with INDIE." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_70Markdown
[Armengol and Plaza. "Induction of Feature Terms with INDIE." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/armengol1997ecml-induction/) doi:10.1007/3-540-62858-4_70BibTeX
@inproceedings{armengol1997ecml-induction,
title = {{Induction of Feature Terms with INDIE}},
author = {Armengol, Eva and Plaza, Enric},
booktitle = {European Conference on Machine Learning},
year = {1997},
pages = {33-48},
doi = {10.1007/3-540-62858-4_70},
url = {https://mlanthology.org/ecmlpkdd/1997/armengol1997ecml-induction/}
}