TY - GEN
T1 - A maximum entropy approach to species distribution modeling
AU - Phillips, Steven J.
AU - Dudík, Miroslav
AU - Schapire, Robert E.
PY - 2004
Y1 - 2004
N2 - We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool, called GARP, on a dataset containing observation data for North American breeding birds. We also study how well maxent performs as a function of the number of training examples and training time, analyze the use of regularization to avoid overfitting when the number of examples is small, and explore the interpretability of models constructed using maxent.
AB - We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool, called GARP, on a dataset containing observation data for North American breeding birds. We also study how well maxent performs as a function of the number of training examples and training time, analyze the use of regularization to avoid overfitting when the number of examples is small, and explore the interpretability of models constructed using maxent.
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UR - http://www.scopus.com/inward/citedby.url?scp=14344261668&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:14344261668
SN - 1581138385
SN - 9781581138382
T3 - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
SP - 655
EP - 662
BT - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
A2 - Greiner, R.
A2 - Schuurmans, D.
T2 - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Y2 - 4 July 2004 through 8 July 2004
ER -