Abstract
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.
Keywords
OverfittingPrinciple of maximum entropyInterpretabilityComputer scienceEnvironmental niche modellingRegularization (linguistics)Machine learningEntropy (arrow of time)Artificial intelligenceData miningEcologyArtificial neural networkBiology
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Publication Info
- Year
- 2004
- Type
- article
- Pages
- 83-83
- Citations
- 2229
- Access
- Closed
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Cite This
Steven J. Phillips,
Miroslav Dudı́k,
Robert E. Schapire
(2004).
A maximum entropy approach to species distribution modeling.
, 83-83.
https://doi.org/10.1145/1015330.1015412
Identifiers
- DOI
- 10.1145/1015330.1015412