Jason H. Moore, Maksim Shestov, Peter Schmitt, Randal S. Olson. A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Marylyn D. Ritchie, Teri E. Klein, editors, Biocomputing 2018: Proceedings of the Pacific Symposium, The Big Island of Hawaii, Hawaii, USA, January 3-7, 2018. pages 259-267, 2018. [doi]
@inproceedings{MooreSSO18,
title = {A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods},
author = {Jason H. Moore and Maksim Shestov and Peter Schmitt and Randal S. Olson},
year = {2018},
url = {http://psb.stanford.edu/psb-online/proceedings/psb18/moore.pdf},
researchr = {https://researchr.org/publication/MooreSSO18},
cites = {0},
citedby = {0},
pages = {259-267},
booktitle = {Biocomputing 2018: Proceedings of the Pacific Symposium, The Big Island of Hawaii, Hawaii, USA, January 3-7, 2018},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Marylyn D. Ritchie and Teri E. Klein},
}