A Formal Approach to Learning from Examples
Abstract
A formal, foundational approach to learning from examples is presented. In the approach, it is assumed that a domain of application is describable as a set of facts, or ground atomic formulae. The task of a learning system is to form and modify hypothesised relations among the relations in the domain, based on a known finite subset of the ground atomic formulae. The subset of known ground atomic formulae is also assumed to grow monotonically, and so the set of hypotheses will require occasional revision. Formal systems are derived by means of which the set of potential hypotheses that can be formed is precisely specified. A procedure is also derived for restoring the consistency of a set of hypotheses after conflicting evidence is encountered. The framework is intended both as a basis for the development of autonomous systems that learn from examples, and as a neutral point from which such systems may be viewed and compared.
Cite
Text
Delgrande. "A Formal Approach to Learning from Examples." International Joint Conference on Artificial Intelligence, 1987. doi:10.1016/S0020-7373(87)80087-1Markdown
[Delgrande. "A Formal Approach to Learning from Examples." International Joint Conference on Artificial Intelligence, 1987.](https://mlanthology.org/ijcai/1987/delgrande1987ijcai-formal/) doi:10.1016/S0020-7373(87)80087-1BibTeX
@inproceedings{delgrande1987ijcai-formal,
title = {{A Formal Approach to Learning from Examples}},
author = {Delgrande, James P.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1987},
pages = {315-322},
doi = {10.1016/S0020-7373(87)80087-1},
url = {https://mlanthology.org/ijcai/1987/delgrande1987ijcai-formal/}
}