Mining Experimental Data for Dynamical Invariants - From Cognitive Robotics to Computational Biology
Abstract
For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful.
Cite
Text
Lipson. "Mining Experimental Data for Dynamical Invariants - From Cognitive Robotics to Computational Biology." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15880-3_4Markdown
[Lipson. "Mining Experimental Data for Dynamical Invariants - From Cognitive Robotics to Computational Biology." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/lipson2010ecmlpkdd-mining/) doi:10.1007/978-3-642-15880-3_4BibTeX
@inproceedings{lipson2010ecmlpkdd-mining,
title = {{Mining Experimental Data for Dynamical Invariants - From Cognitive Robotics to Computational Biology}},
author = {Lipson, Hod},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {4},
doi = {10.1007/978-3-642-15880-3_4},
url = {https://mlanthology.org/ecmlpkdd/2010/lipson2010ecmlpkdd-mining/}
}