Integrating Models of Knowledge and Machine Learning
Abstract
We propose a framework allowing a real integration of Machine Learning and Knowledge acquisition. This paper shows how the input of a Machine Learning system can be mapped to the model of expertise as it is used in KADS methodology. The notion of learning bias will play a central role. We shall see that parts of it can be identified to what KADS's people call the inference and the task models. Doing this conceptual mapping, we give a semantics to most of the inputs of Machine Learning programs in terms of knowledge acquisition models. The ENIGME system which implements this work will be presented
Cite
Text
Ganascia et al. "Integrating Models of Knowledge and Machine Learning." European Conference on Machine Learning, 1993. doi:10.1007/3-540-56602-3_157Markdown
[Ganascia et al. "Integrating Models of Knowledge and Machine Learning." European Conference on Machine Learning, 1993.](https://mlanthology.org/ecmlpkdd/1993/ganascia1993ecml-integrating/) doi:10.1007/3-540-56602-3_157BibTeX
@inproceedings{ganascia1993ecml-integrating,
title = {{Integrating Models of Knowledge and Machine Learning}},
author = {Ganascia, Jean-Gabriel and Thomas, Jérôme and Laublet, Philippe},
booktitle = {European Conference on Machine Learning},
year = {1993},
pages = {396-401},
doi = {10.1007/3-540-56602-3_157},
url = {https://mlanthology.org/ecmlpkdd/1993/ganascia1993ecml-integrating/}
}