Identification

Author

Phillips SJ, Dudik M, Schapire RE

Title

A maximum entropy approach to species distribution modeling

Year

2004

Publication type

In proceedings

Journal

Proceedings of the 21st International Conference on Machine Learning

Created

2014-08-18 21:36:20+00:00

Modified

2016-07-29 20:21:17.797038+00:00

Details

Pages

655-662

Access

Language

English

URL http://www.cs.princeton.edu/~schapire/papers/maxent_icml.pdf
Accessed

2016-07-13

Extended information

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.