@article{Shan:2006:EM,
	title = {Machine learning of poorly predictable ecological
                 data},
	author = {Y. Shan and D. Paull and R. I. McKay},
	journal = {Ecological Modelling},
	month = {15 May},
	note = {Selected Papers from the Third Conference of the
                 International Society for Ecological Informatics
                 (ISEI), August 26--30, 2002, Grottaferrata, Rome,
                 Italy},
	number = {1-2},
	pages = {129--138},
	volume = {195},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2160c07f3f871ab47885e74c23aee167f/brazovayeye},
	abstract = {a variety of machine learning techniques to a
                 difficult modelling problem, the spatial distribution
                 of an endangered Australian marsupial, the southern
                 brown bandicoot (Isoodon obesulus). Four learning
                 techniques decision trees/rules, neural networks,
                 support vector machines and genetic programming were
                 applied to the problem. Support vector and neural
                 network approaches gave marginally better predictivity,
                 but in the context of low overall accuracy, decision
                 trees and genetic programming gave more useful results
                 because of the human comprehensibility of their
                 models.},
	doi = {doi:10.1016/j.ecolmodel.2005.11.015},
	keywords = {Decision Neural Southern Spatial Support algorithms, bandicoot, brown distribution genetic machines, modelling networks, programming, trees, vector }
}