Learning Engineering Models with the Minimum Description Length Principle
Abstract
This paper discusses discovery of mathematical models from engineering data sets. KEDS, a Knowledge-based Equation Discovery System, identifies several potentially overlapping regions in the problem space, each associated with an equation of different complexity and accuracy. The minimum description length principle, together with the KEDS algorithm, is used to guide the partitioning of the problem space. The KEDSMDL algorithm has been tested on discovering models for predicting the performance efficiencies of an internal combustion engine.
Cite
Text
Rao and Lu. "Learning Engineering Models with the Minimum Description Length Principle." AAAI Conference on Artificial Intelligence, 1992.Markdown
[Rao and Lu. "Learning Engineering Models with the Minimum Description Length Principle." AAAI Conference on Artificial Intelligence, 1992.](https://mlanthology.org/aaai/1992/rao1992aaai-learning/)BibTeX
@inproceedings{rao1992aaai-learning,
title = {{Learning Engineering Models with the Minimum Description Length Principle}},
author = {Rao, R. Bharat and Lu, Stephen C. Y.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1992},
pages = {717-722},
url = {https://mlanthology.org/aaai/1992/rao1992aaai-learning/}
}