Knowledge Acquisition Combining Analytical and Empirrcal Techniques
Abstract
In this paper we introduce a methodology for classification-oriented knowledge-base generation using LINNEO, a software for fuzzy classification and rule generation which resorts to analytical -EBG- and empirical -SBL- knowledge acquisition techniques. LINNEO builds a classification from a set of (frequently noisy) observations and a (possibly incomplete) domain theory supplied by the expert. The final result is a fuzzy rules knowledge base. It is believed that integrating both types (EBG, SBL) of learning techniques improves the whole process of knowledge acquistion. In our approach SBL is biased by a domain theory which helps in focusing the induction process.
Cite
Text
Martín et al. "Knowledge Acquisition Combining Analytical and Empirrcal Techniques." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50133-1Markdown
[Martín et al. "Knowledge Acquisition Combining Analytical and Empirrcal Techniques." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/martin1991icml-knowledge/) doi:10.1016/B978-1-55860-200-7.50133-1BibTeX
@inproceedings{martin1991icml-knowledge,
title = {{Knowledge Acquisition Combining Analytical and Empirrcal Techniques}},
author = {Martín, Mario and Sangüesa, Ramon and Cortés, Ulises},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {657-661},
doi = {10.1016/B978-1-55860-200-7.50133-1},
url = {https://mlanthology.org/icml/1991/martin1991icml-knowledge/}
}