Structural Machine Learning with Galois Lattice and Graphs
Abstract
This paper defines a formal approach to learning from examples described by labelled graphs. We propose a formal model based upon lattice theory and in particular with the use of Galois lattice. We enlarge the domain of formal concept analysis, by the use of the Galois lattice model with structural description of examples and concepts. Our implementation, called "Graal" (for GRAph And Learning) constructs a Galois lattice for any description language provided that the two operations of comparison and generalization are determined for that language. We prove that these operations exist in the case of labelled graphs. 1.
Cite
Text
Liquiere and Sallantin. "Structural Machine Learning with Galois Lattice and Graphs." International Conference on Machine Learning, 1998.Markdown
[Liquiere and Sallantin. "Structural Machine Learning with Galois Lattice and Graphs." International Conference on Machine Learning, 1998.](https://mlanthology.org/icml/1998/liquiere1998icml-structural/)BibTeX
@inproceedings{liquiere1998icml-structural,
title = {{Structural Machine Learning with Galois Lattice and Graphs}},
author = {Liquiere, Michel and Sallantin, Jean},
booktitle = {International Conference on Machine Learning},
year = {1998},
pages = {305-313},
url = {https://mlanthology.org/icml/1998/liquiere1998icml-structural/}
}