Metric-Based Inductive Learning Using Semantic Height Functions
Abstract
In the present paper we propose a consistent way to integrate syntactical least general generalizations (lgg’s) with semantic evaluation of the hypotheses. For this purpose we use two different relations on the hypothesis space — a constructive one, used to generate lgg’s and a semantic one giving the coverage-based evaluation of the lgg. These two relations jointly implement a semantic distance measure . The formal background for this is a height-based definition of a semi-distance in a join semi-lattice. We use some basic results from lattice theory and introduce a family of language independent coverage-based height functions. The theoretical results are illustrated by examples of solving some basic inductive learning tasks.
Cite
Text
Markov and Marinchev. "Metric-Based Inductive Learning Using Semantic Height Functions." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_27Markdown
[Markov and Marinchev. "Metric-Based Inductive Learning Using Semantic Height Functions." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/markov2000ecml-metricbased/) doi:10.1007/3-540-45164-1_27BibTeX
@inproceedings{markov2000ecml-metricbased,
title = {{Metric-Based Inductive Learning Using Semantic Height Functions}},
author = {Markov, Zdravko and Marinchev, Ivo},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {254-262},
doi = {10.1007/3-540-45164-1_27},
url = {https://mlanthology.org/ecmlpkdd/2000/markov2000ecml-metricbased/}
}