Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Learning. Pages 810-818.
Abstract
SDS is a discovery system from numeric measurement data. It outperforms the existing systems in every aspect of search efficiency, noise tolerancy, credibility of the resulting equations and complexity of the target system that it can handle. The power of SDS comes from the use of the scale-types of the measurement data and mathematical property of identity by which to constrain the admissible solutions. Its algorithm is described with a complex working example and the performance comparison with other systems are discussed.
@inproceedings{ijcai1997p117,
title = {Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints},
author = {Washio, Takashi and Motoda, Hiroshi},
booktitle = {Proceedings of the Fifteenth International Joint Conference on
Artificial Intelligence, {IJCAI-97}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Martha E. Pollack},
pages = {810--818},
year = {1997},
note = {Learning},
url = {https://ijcai.org/proceedings/1997/117},
}
TY - CPAPER TI - Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints AU - Washio, Takashi AU - Motoda, Hiroshi T2 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97 PB - International Joint Conferences on Artificial Intelligence PY - 1997 SP - 810 EP - 818 UR - https://ijcai.org/proceedings/1997/117 ER -Copied
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